Overview

Dataset statistics

Number of variables28
Number of observations759609
Missing cells0
Missing cells (%)0.0%
Duplicate rows19554
Duplicate rows (%)2.6%
Total size in memory169.0 MiB
Average record size in memory233.3 B

Variable types

Numeric19
Categorical6
Boolean3

Alerts

moon_clearance_complete has constant value "False"Constant
Dataset has 19554 (2.6%) duplicate rowsDuplicates
company_location has a high cardinality: 159 distinct valuesHigh cardinality
id_x is highly overall correlated with shuttle_idHigh correlation
engines is highly overall correlated with shuttle_type and 3 other fieldsHigh correlation
passenger_capacity is highly overall correlated with engine_type and 2 other fieldsHigh correlation
crew is highly overall correlated with engines and 1 other fieldsHigh correlation
price is highly overall correlated with engines and 2 other fieldsHigh correlation
company_id is highly overall correlated with shuttle_location and 6 other fieldsHigh correlation
shuttle_id is highly overall correlated with id_xHigh correlation
review_scores_rating is highly overall correlated with review_scores_comfort and 5 other fieldsHigh correlation
review_scores_comfort is highly overall correlated with review_scores_rating and 4 other fieldsHigh correlation
review_scores_amenities is highly overall correlated with review_scores_rating and 4 other fieldsHigh correlation
review_scores_trip is highly overall correlated with review_scores_rating and 4 other fieldsHigh correlation
review_scores_crew is highly overall correlated with review_scores_rating and 5 other fieldsHigh correlation
review_scores_price is highly overall correlated with review_scores_rating and 5 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
id_y is highly overall correlated with shuttle_location and 6 other fieldsHigh correlation
total_fleet_count is highly overall correlated with shuttle_location and 4 other fieldsHigh correlation
d_check_complete is highly overall correlated with company_id and 2 other fieldsHigh correlation
iata_approved is highly overall correlated with d_check_complete and 2 other fieldsHigh correlation
review_scores_location is highly overall correlated with review_scores_rating and 2 other fieldsHigh correlation
shuttle_location is highly overall correlated with engine_type and 3 other fieldsHigh correlation
shuttle_type is highly overall correlated with engines and 3 other fieldsHigh correlation
engine_type is highly overall correlated with shuttle_location and 3 other fieldsHigh correlation
engines has 40534 (5.3%) zerosZeros

Reproduction

Analysis started2022-11-24 14:46:49.926816
Analysis finished2022-11-24 14:50:51.665598
Duration4 minutes and 1.74 second
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

id_x
Real number (ℝ)

Distinct29768
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38504.629
Minimum4
Maximum77095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:51.826021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4054
Q119107
median39049
Q358363
95-th percentile72358
Maximum77095
Range77091
Interquartile range (IQR)39256

Descriptive statistics

Standard deviation22169.907
Coefficient of variation (CV)0.57577251
Kurtosis-1.2108859
Mean38504.629
Median Absolute Deviation (MAD)19612
Skewness-0.017028223
Sum2.9248463 × 1010
Variance4.9150476 × 108
MonotonicityNot monotonic
2022-11-24T14:50:51.994990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8692 1086
 
0.1%
29483 1086
 
0.1%
52116 1086
 
0.1%
25234 1086
 
0.1%
35034 1086
 
0.1%
41758 1086
 
0.1%
17273 1086
 
0.1%
13084 1086
 
0.1%
19472 1086
 
0.1%
18120 1086
 
0.1%
Other values (29758) 748749
98.6%
ValueCountFrequency (%)
4 3
 
< 0.1%
7 8
< 0.1%
9 3
 
< 0.1%
11 3
 
< 0.1%
25 1
 
< 0.1%
26 19
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
77095 1
 
< 0.1%
77094 60
< 0.1%
77088 14
 
< 0.1%
77087 4
 
< 0.1%
77085 14
 
< 0.1%
77083 11
 
< 0.1%
77078 1
 
< 0.1%
77076 7
 
< 0.1%
77072 2
 
< 0.1%
77069 53
< 0.1%

shuttle_location
Categorical

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Barbados
120776 
Micronesia
109398 
Malta
106708 
Nicaragua
92759 
Rwanda
69383 
Other values (25)
260585 

Length

Max length25
Median length18
Mean length10.284496
Min length4

Characters and Unicode

Total characters7812196
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
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 rowNiue
2nd rowNiue
3rd rowNiue
4th rowNiue
5th rowNiue

Common Values

ValueCountFrequency (%)
Barbados 120776
15.9%
Micronesia 109398
14.4%
Malta 106708
14.0%
Nicaragua 92759
12.2%
Rwanda 69383
9.1%
Russian Federation 58490
7.7%
Sao Tome and Principe 50293
6.6%
United Kingdom 28488
 
3.8%
Niue 26269
 
3.5%
Bouvet Island (Bouvetoya) 23346
 
3.1%
Other values (20) 73699
9.7%

Length

2022-11-24T14:50:52.157789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
barbados 120776
 
11.1%
micronesia 109398
 
10.1%
malta 106708
 
9.8%
nicaragua 92759
 
8.5%
rwanda 69383
 
6.4%
and 65920
 
6.1%
russian 58490
 
5.4%
federation 58490
 
5.4%
sao 50293
 
4.6%
tome 50293
 
4.6%
Other values (34) 303926
28.0%

Most occurring characters

ValueCountFrequency (%)
a 1365388
17.5%
i 699351
 
9.0%
n 556932
 
7.1%
o 511438
 
6.5%
e 457654
 
5.9%
r 448070
 
5.7%
s 420122
 
5.4%
d 410804
 
5.3%
326827
 
4.2%
t 278741
 
3.6%
Other values (34) 2336869
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6419303
82.2%
Uppercase Letter 1019374
 
13.0%
Space Separator 326827
 
4.2%
Open Punctuation 23346
 
0.3%
Close Punctuation 23346
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1365388
21.3%
i 699351
10.9%
n 556932
8.7%
o 511438
 
8.0%
e 457654
 
7.1%
r 448070
 
7.0%
s 420122
 
6.5%
d 410804
 
6.4%
t 278741
 
4.3%
u 265663
 
4.1%
Other values (14) 1005140
15.7%
Uppercase Letter
ValueCountFrequency (%)
M 217945
21.4%
B 168774
16.6%
R 131930
12.9%
N 119028
11.7%
F 77298
 
7.6%
S 51370
 
5.0%
P 50514
 
5.0%
T 50293
 
4.9%
I 39155
 
3.8%
K 38419
 
3.8%
Other values (7) 74648
 
7.3%
Space Separator
ValueCountFrequency (%)
326827
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23346
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7438677
95.2%
Common 373519
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1365388
18.4%
i 699351
 
9.4%
n 556932
 
7.5%
o 511438
 
6.9%
e 457654
 
6.2%
r 448070
 
6.0%
s 420122
 
5.6%
d 410804
 
5.5%
t 278741
 
3.7%
u 265663
 
3.6%
Other values (31) 2024514
27.2%
Common
ValueCountFrequency (%)
326827
87.5%
( 23346
 
6.3%
) 23346
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7812196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1365388
17.5%
i 699351
 
9.0%
n 556932
 
7.1%
o 511438
 
6.5%
e 457654
 
5.9%
r 448070
 
5.7%
s 420122
 
5.4%
d 410804
 
5.3%
326827
 
4.2%
t 278741
 
3.6%
Other values (34) 2336869
29.9%

shuttle_type
Categorical

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Type V5
504840 
Type F5
187107 
Type G0
 
48629
Type V2
 
8887
Type Z6
 
2779
Other values (27)
 
7367

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5317263
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowType V5
2nd rowType V5
3rd rowType V5
4th rowType V5
5th rowType V5

Common Values

ValueCountFrequency (%)
Type V5 504840
66.5%
Type F5 187107
 
24.6%
Type G0 48629
 
6.4%
Type V2 8887
 
1.2%
Type Z6 2779
 
0.4%
Type O3 1876
 
0.2%
Type V7 1500
 
0.2%
Type N0 1323
 
0.2%
Type X3 547
 
0.1%
Type E3 474
 
0.1%
Other values (22) 1647
 
0.2%

Length

2022-11-24T14:50:52.289358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
type 759609
50.0%
v5 504840
33.2%
f5 187107
 
12.3%
g0 48629
 
3.2%
v2 8887
 
0.6%
z6 2779
 
0.2%
o3 1876
 
0.1%
v7 1500
 
0.1%
n0 1323
 
0.1%
x3 547
 
< 0.1%
Other values (23) 2121
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 759636
14.3%
y 759609
14.3%
p 759609
14.3%
e 759609
14.3%
759609
14.3%
5 692259
13.0%
V 515227
9.7%
F 187456
 
3.5%
0 49971
 
0.9%
G 48629
 
0.9%
Other values (23) 25649
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2278827
42.9%
Uppercase Letter 1519218
28.6%
Space Separator 759609
 
14.3%
Decimal Number 759609
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 759636
50.0%
V 515227
33.9%
F 187456
 
12.3%
G 48629
 
3.2%
Z 2832
 
0.2%
O 1884
 
0.1%
N 1323
 
0.1%
X 547
 
< 0.1%
E 474
 
< 0.1%
W 287
 
< 0.1%
Other values (11) 923
 
0.1%
Decimal Number
ValueCountFrequency (%)
5 692259
91.1%
0 49971
 
6.6%
2 8893
 
1.2%
3 2897
 
0.4%
6 2799
 
0.4%
7 2225
 
0.3%
1 510
 
0.1%
4 55
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
y 759609
33.3%
p 759609
33.3%
e 759609
33.3%
Space Separator
ValueCountFrequency (%)
759609
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3798045
71.4%
Common 1519218
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 759636
20.0%
y 759609
20.0%
p 759609
20.0%
e 759609
20.0%
V 515227
13.6%
F 187456
 
4.9%
G 48629
 
1.3%
Z 2832
 
0.1%
O 1884
 
< 0.1%
N 1323
 
< 0.1%
Other values (14) 2231
 
0.1%
Common
ValueCountFrequency (%)
759609
50.0%
5 692259
45.6%
0 49971
 
3.3%
2 8893
 
0.6%
3 2897
 
0.2%
6 2799
 
0.2%
7 2225
 
0.1%
1 510
 
< 0.1%
4 55
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5317263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 759636
14.3%
y 759609
14.3%
p 759609
14.3%
e 759609
14.3%
759609
14.3%
5 692259
13.0%
V 515227
9.7%
F 187456
 
3.5%
0 49971
 
0.9%
G 48629
 
0.9%
Other values (23) 25649
 
0.5%

engine_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Plasma
669699 
Quantum
83227 
Nuclear
 
6683

Length

Max length7
Median length6
Mean length6.1183635
Min length6

Characters and Unicode

Total characters4647564
Distinct characters13
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 rowQuantum
2nd rowQuantum
3rd rowQuantum
4th rowQuantum
5th rowQuantum

Common Values

ValueCountFrequency (%)
Plasma 669699
88.2%
Quantum 83227
 
11.0%
Nuclear 6683
 
0.9%

Length

2022-11-24T14:50:52.411809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-24T14:50:52.574533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
plasma 669699
88.2%
quantum 83227
 
11.0%
nuclear 6683
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a 1429308
30.8%
m 752926
16.2%
l 676382
14.6%
P 669699
14.4%
s 669699
14.4%
u 173137
 
3.7%
Q 83227
 
1.8%
n 83227
 
1.8%
t 83227
 
1.8%
N 6683
 
0.1%
Other values (3) 20049
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3887955
83.7%
Uppercase Letter 759609
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1429308
36.8%
m 752926
19.4%
l 676382
17.4%
s 669699
17.2%
u 173137
 
4.5%
n 83227
 
2.1%
t 83227
 
2.1%
c 6683
 
0.2%
e 6683
 
0.2%
r 6683
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P 669699
88.2%
Q 83227
 
11.0%
N 6683
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4647564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1429308
30.8%
m 752926
16.2%
l 676382
14.6%
P 669699
14.4%
s 669699
14.4%
u 173137
 
3.7%
Q 83227
 
1.8%
n 83227
 
1.8%
t 83227
 
1.8%
N 6683
 
0.1%
Other values (3) 20049
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4647564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1429308
30.8%
m 752926
16.2%
l 676382
14.6%
P 669699
14.4%
s 669699
14.4%
u 173137
 
3.7%
Q 83227
 
1.8%
n 83227
 
1.8%
t 83227
 
1.8%
N 6683
 
0.1%
Other values (3) 20049
 
0.4%

engine_vendor
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
ThetaBase Services
758528 
Banks, Wood and Phillips
 
797
Warwick Technology Multinational
 
168
SIT Technology Unlimited
 
81
MCW Global
 
35

Length

Max length32
Median length18
Mean length18.009663
Min length10

Characters and Unicode

Total characters13680302
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
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 rowThetaBase Services
2nd rowThetaBase Services
3rd rowThetaBase Services
4th rowThetaBase Services
5th rowBanks, Wood and Phillips

Common Values

ValueCountFrequency (%)
ThetaBase Services 758528
99.9%
Banks, Wood and Phillips 797
 
0.1%
Warwick Technology Multinational 168
 
< 0.1%
SIT Technology Unlimited 81
 
< 0.1%
MCW Global 35
 
< 0.1%

Length

2022-11-24T14:50:52.690448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-24T14:50:52.844226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
thetabase 758528
49.9%
services 758528
49.9%
banks 797
 
0.1%
wood 797
 
0.1%
and 797
 
0.1%
phillips 797
 
0.1%
technology 249
 
< 0.1%
warwick 168
 
< 0.1%
multinational 168
 
< 0.1%
sit 81
 
< 0.1%
Other values (3) 151
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 3034442
22.2%
a 1519189
11.1%
s 1518650
11.1%
761452
 
5.6%
i 760788
 
5.6%
h 759574
 
5.6%
B 759325
 
5.6%
t 758945
 
5.5%
c 758945
 
5.5%
T 758858
 
5.5%
Other values (23) 2290134
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10639029
77.8%
Uppercase Letter 2279024
 
16.7%
Space Separator 761452
 
5.6%
Other Punctuation 797
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3034442
28.5%
a 1519189
14.3%
s 1518650
14.3%
i 760788
 
7.2%
h 759574
 
7.1%
t 758945
 
7.1%
c 758945
 
7.1%
r 758696
 
7.1%
v 758528
 
7.1%
l 2330
 
< 0.1%
Other values (11) 8942
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 759325
33.3%
T 758858
33.3%
S 758609
33.3%
W 1000
 
< 0.1%
P 797
 
< 0.1%
M 203
 
< 0.1%
I 81
 
< 0.1%
U 81
 
< 0.1%
C 35
 
< 0.1%
G 35
 
< 0.1%
Space Separator
ValueCountFrequency (%)
761452
100.0%
Other Punctuation
ValueCountFrequency (%)
, 797
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12918053
94.4%
Common 762249
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3034442
23.5%
a 1519189
11.8%
s 1518650
11.8%
i 760788
 
5.9%
h 759574
 
5.9%
B 759325
 
5.9%
t 758945
 
5.9%
c 758945
 
5.9%
T 758858
 
5.9%
r 758696
 
5.9%
Other values (21) 1530641
11.8%
Common
ValueCountFrequency (%)
761452
99.9%
, 797
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13680302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3034442
22.2%
a 1519189
11.1%
s 1518650
11.1%
761452
 
5.6%
i 760788
 
5.6%
h 759574
 
5.6%
B 759325
 
5.6%
t 758945
 
5.5%
c 758945
 
5.5%
T 758858
 
5.5%
Other values (23) 2290134
16.7%

engines
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0726413
Minimum0
Maximum12
Zeros40534
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:52.960109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2778244
Coefficient of variation (CV)0.61651981
Kurtosis0.19454499
Mean2.0726413
Median Absolute Deviation (MAD)1
Skewness0.75108798
Sum1574397
Variance1.6328353
MonotonicityNot monotonic
2022-11-24T14:50:53.076021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 260579
34.3%
2 222290
29.3%
3 118778
15.6%
4 81211
 
10.7%
0 40534
 
5.3%
5 30588
 
4.0%
6 4415
 
0.6%
7 1141
 
0.2%
8 51
 
< 0.1%
12 9
 
< 0.1%
Other values (3) 13
 
< 0.1%
ValueCountFrequency (%)
0 40534
 
5.3%
1 260579
34.3%
2 222290
29.3%
3 118778
15.6%
4 81211
 
10.7%
5 30588
 
4.0%
6 4415
 
0.6%
7 1141
 
0.2%
8 51
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
12 9
 
< 0.1%
11 3
 
< 0.1%
10 4
 
< 0.1%
9 6
 
< 0.1%
8 51
 
< 0.1%
7 1141
 
0.2%
6 4415
 
0.6%
5 30588
 
4.0%
4 81211
10.7%
3 118778
15.6%

passenger_capacity
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7194873
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:53.198463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3630074
Coefficient of variation (CV)0.50069156
Kurtosis0.51704786
Mean4.7194873
Median Absolute Deviation (MAD)2
Skewness0.77995214
Sum3584965
Variance5.5838042
MonotonicityNot monotonic
2022-11-24T14:50:53.314396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 235339
31.0%
2 145575
19.2%
6 114644
15.1%
8 66212
 
8.7%
5 47306
 
6.2%
3 42218
 
5.6%
7 35721
 
4.7%
1 23129
 
3.0%
10 20230
 
2.7%
9 19306
 
2.5%
Other values (7) 9929
 
1.3%
ValueCountFrequency (%)
1 23129
 
3.0%
2 145575
19.2%
3 42218
 
5.6%
4 235339
31.0%
5 47306
 
6.2%
6 114644
15.1%
7 35721
 
4.7%
8 66212
 
8.7%
9 19306
 
2.5%
10 20230
 
2.7%
ValueCountFrequency (%)
20 6
 
< 0.1%
16 402
 
0.1%
15 62
 
< 0.1%
14 1455
 
0.2%
13 447
 
0.1%
12 5695
 
0.7%
11 1862
 
0.2%
10 20230
 
2.7%
9 19306
 
2.5%
8 66212
8.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
strict
673660 
moderate
 
55883
flexible
 
30066

Length

Max length8
Median length6
Mean length6.226298
Min length6

Characters and Unicode

Total characters4729552
Distinct characters14
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 rowstrict
2nd rowstrict
3rd rowstrict
4th rowstrict
5th rowstrict

Common Values

ValueCountFrequency (%)
strict 673660
88.7%
moderate 55883
 
7.4%
flexible 30066
 
4.0%

Length

2022-11-24T14:50:53.445945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-24T14:50:53.599636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
strict 673660
88.7%
moderate 55883
 
7.4%
flexible 30066
 
4.0%

Most occurring characters

ValueCountFrequency (%)
t 1403203
29.7%
r 729543
15.4%
i 703726
14.9%
s 673660
14.2%
c 673660
14.2%
e 171898
 
3.6%
l 60132
 
1.3%
m 55883
 
1.2%
o 55883
 
1.2%
d 55883
 
1.2%
Other values (4) 146081
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4729552
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1403203
29.7%
r 729543
15.4%
i 703726
14.9%
s 673660
14.2%
c 673660
14.2%
e 171898
 
3.6%
l 60132
 
1.3%
m 55883
 
1.2%
o 55883
 
1.2%
d 55883
 
1.2%
Other values (4) 146081
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4729552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1403203
29.7%
r 729543
15.4%
i 703726
14.9%
s 673660
14.2%
c 673660
14.2%
e 171898
 
3.6%
l 60132
 
1.3%
m 55883
 
1.2%
o 55883
 
1.2%
d 55883
 
1.2%
Other values (4) 146081
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4729552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1403203
29.7%
r 729543
15.4%
i 703726
14.9%
s 673660
14.2%
c 673660
14.2%
e 171898
 
3.6%
l 60132
 
1.3%
m 55883
 
1.2%
o 55883
 
1.2%
d 55883
 
1.2%
Other values (4) 146081
 
3.1%

crew
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6232838
Minimum0
Maximum20
Zeros5757
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:54.031345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6094034
Coefficient of variation (CV)0.61350717
Kurtosis2.2846916
Mean2.6232838
Median Absolute Deviation (MAD)1
Skewness1.198889
Sum1992670
Variance2.5901794
MonotonicityNot monotonic
2022-11-24T14:50:54.152654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 217973
28.7%
1 209179
27.5%
3 133015
17.5%
4 93646
12.3%
5 55912
 
7.4%
6 26160
 
3.4%
7 11789
 
1.6%
0 5757
 
0.8%
8 2905
 
0.4%
9 2508
 
0.3%
Other values (8) 765
 
0.1%
ValueCountFrequency (%)
0 5757
 
0.8%
1 209179
27.5%
2 217973
28.7%
3 133015
17.5%
4 93646
12.3%
5 55912
 
7.4%
6 26160
 
3.4%
7 11789
 
1.6%
8 2905
 
0.4%
9 2508
 
0.3%
ValueCountFrequency (%)
20 26
 
< 0.1%
18 2
 
< 0.1%
16 68
 
< 0.1%
15 21
 
< 0.1%
14 47
 
< 0.1%
12 211
 
< 0.1%
11 12
 
< 0.1%
10 378
 
< 0.1%
9 2508
0.3%
8 2905
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
True
563118 
False
196491 
ValueCountFrequency (%)
True 563118
74.1%
False 196491
 
25.9%
2022-11-24T14:50:54.294237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
False
759609 
ValueCountFrequency (%)
False 759609
100.0%
2022-11-24T14:50:54.410159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

price
Real number (ℝ)

Distinct527
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3503.8751
Minimum870
Maximum86150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:54.528520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum870
5-th percentile1260
Q12417
median3145
Q34146
95-th percentile6707
Maximum86150
Range85280
Interquartile range (IQR)1729

Descriptive statistics

Standard deviation1866.609
Coefficient of variation (CV)0.53272704
Kurtosis58.018955
Mean3503.8751
Median Absolute Deviation (MAD)910
Skewness3.3383237
Sum2.6615751 × 109
Variance3484229.3
MonotonicityNot monotonic
2022-11-24T14:50:54.679700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2430 17088
 
2.2%
1130 16376
 
2.2%
3457 15414
 
2.0%
2170 12943
 
1.7%
3015 10687
 
1.4%
2820 10583
 
1.4%
3470 8206
 
1.1%
1910 8169
 
1.1%
1195 7887
 
1.0%
2677 7502
 
1.0%
Other values (517) 644754
84.9%
ValueCountFrequency (%)
870 193
 
< 0.1%
961 2
 
< 0.1%
974 9
 
< 0.1%
1000 8
 
< 0.1%
1013 28
 
< 0.1%
1026 90
 
< 0.1%
1039 112
 
< 0.1%
1052 89
 
< 0.1%
1065 647
0.1%
1078 462
0.1%
ValueCountFrequency (%)
86150 9
 
< 0.1%
46370 1
 
< 0.1%
37270 1
 
< 0.1%
33370 53
< 0.1%
24920 30
< 0.1%
24270 2
 
< 0.1%
20370 6
 
< 0.1%
19720 11
 
< 0.1%
19070 8
 
< 0.1%
17120 18
 
< 0.1%

company_id
Real number (ℝ)

Distinct15354
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26968.063
Minimum4
Maximum50094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:54.842520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6838
Q122721
median29647
Q329647
95-th percentile42615
Maximum50094
Range50090
Interquartile range (IQR)6926

Descriptive statistics

Standard deviation9058.3928
Coefficient of variation (CV)0.33589334
Kurtosis1.1296658
Mean26968.063
Median Absolute Deviation (MAD)0
Skewness-0.66148146
Sum2.0485183 × 1010
Variance82054480
MonotonicityNot monotonic
2022-11-24T14:50:54.996210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29647 383358
50.5%
28828 29808
 
3.9%
32203 24288
 
3.2%
20334 23881
 
3.1%
4745 12996
 
1.7%
18077 10625
 
1.4%
10711 10476
 
1.4%
19019 9792
 
1.3%
18459 9216
 
1.2%
15004 7744
 
1.0%
Other values (15344) 237425
31.3%
ValueCountFrequency (%)
4 1
 
< 0.1%
9 4
< 0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
26 2
 
< 0.1%
30 1
 
< 0.1%
36 9
< 0.1%
41 4
< 0.1%
ValueCountFrequency (%)
50094 1
 
< 0.1%
50089 4
 
< 0.1%
50085 1
 
< 0.1%
50080 9
 
< 0.1%
50078 1
 
< 0.1%
50074 64
< 0.1%
50072 9
 
< 0.1%
50071 1
 
< 0.1%
50070 1
 
< 0.1%
50063 1
 
< 0.1%

shuttle_id
Real number (ℝ)

Distinct29768
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38504.629
Minimum4
Maximum77095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:55.165518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4054
Q119107
median39049
Q358363
95-th percentile72358
Maximum77095
Range77091
Interquartile range (IQR)39256

Descriptive statistics

Standard deviation22169.907
Coefficient of variation (CV)0.57577251
Kurtosis-1.2108859
Mean38504.629
Median Absolute Deviation (MAD)19612
Skewness-0.017028223
Sum2.9248463 × 1010
Variance4.9150476 × 108
MonotonicityNot monotonic
2022-11-24T14:50:55.328329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8692 1086
 
0.1%
29483 1086
 
0.1%
52116 1086
 
0.1%
25234 1086
 
0.1%
35034 1086
 
0.1%
41758 1086
 
0.1%
17273 1086
 
0.1%
13084 1086
 
0.1%
19472 1086
 
0.1%
18120 1086
 
0.1%
Other values (29758) 748749
98.6%
ValueCountFrequency (%)
4 3
 
< 0.1%
7 8
< 0.1%
9 3
 
< 0.1%
11 3
 
< 0.1%
25 1
 
< 0.1%
26 19
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
77095 1
 
< 0.1%
77094 60
< 0.1%
77088 14
 
< 0.1%
77087 4
 
< 0.1%
77085 14
 
< 0.1%
77083 11
 
< 0.1%
77078 1
 
< 0.1%
77076 7
 
< 0.1%
77072 2
 
< 0.1%
77069 53
< 0.1%

review_scores_rating
Real number (ℝ)

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.139768
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:55.497658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile60
Q180
median90
Q3100
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.423344
Coefficient of variation (CV)0.15229612
Kurtosis6.1944853
Mean88.139768
Median Absolute Deviation (MAD)10
Skewness-2.0437751
Sum66951761
Variance180.18617
MonotonicityNot monotonic
2022-11-24T14:50:55.660455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 225341
29.7%
80 125109
16.5%
90 60398
 
8.0%
93 33046
 
4.4%
95 24243
 
3.2%
87 23592
 
3.1%
96 21353
 
2.8%
60 19800
 
2.6%
92 15768
 
2.1%
89 15388
 
2.0%
Other values (44) 195571
25.7%
ValueCountFrequency (%)
20 6516
0.9%
27 20
 
< 0.1%
30 49
 
< 0.1%
40 10651
1.4%
45 3
 
< 0.1%
47 138
 
< 0.1%
48 59
 
< 0.1%
50 991
 
0.1%
52 1086
 
0.1%
53 384
 
0.1%
ValueCountFrequency (%)
100 225341
29.7%
99 3309
 
0.4%
98 11465
 
1.5%
97 13194
 
1.7%
96 21353
 
2.8%
95 24243
 
3.2%
94 13044
 
1.7%
93 33046
 
4.4%
92 15768
 
2.1%
91 13208
 
1.7%

review_scores_comfort
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0526811
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:55.798533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4072309
Coefficient of variation (CV)0.15544908
Kurtosis7.3092783
Mean9.0526811
Median Absolute Deviation (MAD)0
Skewness-2.374577
Sum6876498
Variance1.9802989
MonotonicityNot monotonic
2022-11-24T14:50:55.914468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 389432
51.3%
9 191509
25.2%
8 109788
 
14.5%
6 33994
 
4.5%
7 17598
 
2.3%
2 9064
 
1.2%
4 5969
 
0.8%
5 2187
 
0.3%
3 68
 
< 0.1%
ValueCountFrequency (%)
2 9064
 
1.2%
3 68
 
< 0.1%
4 5969
 
0.8%
5 2187
 
0.3%
6 33994
 
4.5%
7 17598
 
2.3%
8 109788
 
14.5%
9 191509
25.2%
10 389432
51.3%
ValueCountFrequency (%)
10 389432
51.3%
9 191509
25.2%
8 109788
 
14.5%
7 17598
 
2.3%
6 33994
 
4.5%
5 2187
 
0.3%
4 5969
 
0.8%
3 68
 
< 0.1%
2 9064
 
1.2%

review_scores_amenities
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.041291
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:56.046002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3751226
Coefficient of variation (CV)0.15209361
Kurtosis6.0896619
Mean9.041291
Median Absolute Deviation (MAD)0
Skewness-2.1648872
Sum6867846
Variance1.8909621
MonotonicityNot monotonic
2022-11-24T14:50:56.161924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 384767
50.7%
9 189878
25.0%
8 109769
 
14.5%
6 30938
 
4.1%
7 25844
 
3.4%
4 6670
 
0.9%
2 6201
 
0.8%
5 5439
 
0.7%
3 103
 
< 0.1%
ValueCountFrequency (%)
2 6201
 
0.8%
3 103
 
< 0.1%
4 6670
 
0.9%
5 5439
 
0.7%
6 30938
 
4.1%
7 25844
 
3.4%
8 109769
 
14.5%
9 189878
25.0%
10 384767
50.7%
ValueCountFrequency (%)
10 384767
50.7%
9 189878
25.0%
8 109769
 
14.5%
7 25844
 
3.4%
6 30938
 
4.1%
5 5439
 
0.7%
4 6670
 
0.9%
3 103
 
< 0.1%
2 6201
 
0.8%

review_scores_trip
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0470571
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:56.284415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5232991
Coefficient of variation (CV)0.1683751
Kurtosis6.6676864
Mean9.0470571
Median Absolute Deviation (MAD)0
Skewness-2.4104593
Sum6872226
Variance2.3204402
MonotonicityNot monotonic
2022-11-24T14:50:56.400324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 411299
54.1%
9 180026
23.7%
8 90890
 
12.0%
6 29118
 
3.8%
7 20843
 
2.7%
4 13465
 
1.8%
2 10767
 
1.4%
5 3138
 
0.4%
3 63
 
< 0.1%
ValueCountFrequency (%)
2 10767
 
1.4%
3 63
 
< 0.1%
4 13465
 
1.8%
5 3138
 
0.4%
6 29118
 
3.8%
7 20843
 
2.7%
8 90890
 
12.0%
9 180026
23.7%
10 411299
54.1%
ValueCountFrequency (%)
10 411299
54.1%
9 180026
23.7%
8 90890
 
12.0%
7 20843
 
2.7%
6 29118
 
3.8%
5 3138
 
0.4%
4 13465
 
1.8%
3 63
 
< 0.1%
2 10767
 
1.4%

review_scores_crew
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1330895
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:56.531601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4460103
Coefficient of variation (CV)0.15832652
Kurtosis7.5445028
Mean9.1330895
Median Absolute Deviation (MAD)0
Skewness-2.5006075
Sum6937577
Variance2.0909458
MonotonicityNot monotonic
2022-11-24T14:50:56.647504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 440561
58.0%
9 159370
 
21.0%
8 90224
 
11.9%
6 36786
 
4.8%
7 14003
 
1.8%
2 9853
 
1.3%
4 6486
 
0.9%
5 2240
 
0.3%
3 86
 
< 0.1%
ValueCountFrequency (%)
2 9853
 
1.3%
3 86
 
< 0.1%
4 6486
 
0.9%
5 2240
 
0.3%
6 36786
 
4.8%
7 14003
 
1.8%
8 90224
 
11.9%
9 159370
 
21.0%
10 440561
58.0%
ValueCountFrequency (%)
10 440561
58.0%
9 159370
 
21.0%
8 90224
 
11.9%
7 14003
 
1.8%
6 36786
 
4.8%
5 2240
 
0.3%
4 6486
 
0.9%
3 86
 
< 0.1%
2 9853
 
1.3%

review_scores_location
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3581461
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:56.769921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1169967
Coefficient of variation (CV)0.1193609
Kurtosis9.8498643
Mean9.3581461
Median Absolute Deviation (MAD)0
Skewness-2.7231754
Sum7108532
Variance1.2476817
MonotonicityNot monotonic
2022-11-24T14:50:56.932860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 474944
62.5%
9 169755
 
22.3%
8 78401
 
10.3%
6 16076
 
2.1%
7 9768
 
1.3%
4 8653
 
1.1%
2 1805
 
0.2%
5 207
 
< 0.1%
ValueCountFrequency (%)
2 1805
 
0.2%
4 8653
 
1.1%
5 207
 
< 0.1%
6 16076
 
2.1%
7 9768
 
1.3%
8 78401
 
10.3%
9 169755
 
22.3%
10 474944
62.5%
ValueCountFrequency (%)
10 474944
62.5%
9 169755
 
22.3%
8 78401
 
10.3%
7 9768
 
1.3%
6 16076
 
2.1%
5 207
 
< 0.1%
4 8653
 
1.1%
2 1805
 
0.2%

review_scores_price
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6967848
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:57.071246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median9
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4170067
Coefficient of variation (CV)0.16293455
Kurtosis5.1504235
Mean8.6967848
Median Absolute Deviation (MAD)1
Skewness-1.8956897
Sum6606156
Variance2.007908
MonotonicityNot monotonic
2022-11-24T14:50:57.187170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 253004
33.3%
10 241909
31.8%
8 175424
23.1%
6 40786
 
5.4%
7 26930
 
3.5%
4 9912
 
1.3%
2 8090
 
1.1%
5 3461
 
0.5%
3 93
 
< 0.1%
ValueCountFrequency (%)
2 8090
 
1.1%
3 93
 
< 0.1%
4 9912
 
1.3%
5 3461
 
0.5%
6 40786
 
5.4%
7 26930
 
3.5%
8 175424
23.1%
9 253004
33.3%
10 241909
31.8%
ValueCountFrequency (%)
10 241909
31.8%
9 253004
33.3%
8 175424
23.1%
7 26930
 
3.5%
6 40786
 
5.4%
5 3461
 
0.5%
4 9912
 
1.3%
3 93
 
< 0.1%
2 8090
 
1.1%

number_of_reviews
Real number (ℝ)

Distinct358
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.297036
Minimum1
Maximum578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:57.334350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q39
95-th percentile45
Maximum578
Range577
Interquartile range (IQR)8

Descriptive statistics

Standard deviation23.048411
Coefficient of variation (CV)2.238354
Kurtosis70.719239
Mean10.297036
Median Absolute Deviation (MAD)2
Skewness6.736901
Sum7821721
Variance531.22926
MonotonicityNot monotonic
2022-11-24T14:50:57.488053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 244576
32.2%
2 116762
15.4%
3 60017
 
7.9%
4 49592
 
6.5%
5 33117
 
4.4%
6 23197
 
3.1%
7 18693
 
2.5%
8 17732
 
2.3%
9 14408
 
1.9%
11 12588
 
1.7%
Other values (348) 168927
22.2%
ValueCountFrequency (%)
1 244576
32.2%
2 116762
15.4%
3 60017
 
7.9%
4 49592
 
6.5%
5 33117
 
4.4%
6 23197
 
3.1%
7 18693
 
2.5%
8 17732
 
2.3%
9 14408
 
1.9%
10 11357
 
1.5%
ValueCountFrequency (%)
578 4
< 0.1%
529 3
< 0.1%
507 4
< 0.1%
501 1
 
< 0.1%
481 4
< 0.1%
471 1
 
< 0.1%
468 1
 
< 0.1%
467 2
< 0.1%
461 1
 
< 0.1%
456 1
 
< 0.1%

reviews_per_month
Real number (ℝ)

Distinct899
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83372731
Minimum0.01
Maximum16.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:57.650853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.16
median0.4
Q31
95-th percentile3.14
Maximum16.56
Range16.55
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation1.1109912
Coefficient of variation (CV)1.3325594
Kurtosis10.780633
Mean0.83372731
Median Absolute Deviation (MAD)0.3
Skewness2.7770527
Sum633306.77
Variance1.2343014
MonotonicityNot monotonic
2022-11-24T14:50:57.806277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 28993
 
3.8%
0.04 20840
 
2.7%
0.09 20085
 
2.6%
0.11 16456
 
2.2%
0.2 16083
 
2.1%
0.07 15663
 
2.1%
0.24 14789
 
1.9%
1 13604
 
1.8%
0.13 12844
 
1.7%
0.03 12598
 
1.7%
Other values (889) 587654
77.4%
ValueCountFrequency (%)
0.01 9
 
< 0.1%
0.02 246
 
< 0.1%
0.03 12598
1.7%
0.04 20840
2.7%
0.05 6892
 
0.9%
0.06 28993
3.8%
0.07 15663
2.1%
0.08 10374
 
1.4%
0.09 20085
2.6%
0.1 11083
 
1.5%
ValueCountFrequency (%)
16.56 6
< 0.1%
15.69 3
 
< 0.1%
14.13 3
 
< 0.1%
13.26 1
 
< 0.1%
12.6 4
< 0.1%
12.59 1
 
< 0.1%
12.5 1
 
< 0.1%
12.39 8
< 0.1%
12.21 2
 
< 0.1%
12.12 2
 
< 0.1%

id_y
Real number (ℝ)

Distinct15354
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26968.063
Minimum4
Maximum50094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:57.989188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6838
Q122721
median29647
Q329647
95-th percentile42615
Maximum50094
Range50090
Interquartile range (IQR)6926

Descriptive statistics

Standard deviation9058.3928
Coefficient of variation (CV)0.33589334
Kurtosis1.1296658
Mean26968.063
Median Absolute Deviation (MAD)0
Skewness-0.66148146
Sum2.0485183 × 1010
Variance82054480
MonotonicityNot monotonic
2022-11-24T14:50:58.168251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29647 383358
50.5%
28828 29808
 
3.9%
32203 24288
 
3.2%
20334 23881
 
3.1%
4745 12996
 
1.7%
18077 10625
 
1.4%
10711 10476
 
1.4%
19019 9792
 
1.3%
18459 9216
 
1.2%
15004 7744
 
1.0%
Other values (15344) 237425
31.3%
ValueCountFrequency (%)
4 1
 
< 0.1%
9 4
< 0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
26 2
 
< 0.1%
30 1
 
< 0.1%
36 9
< 0.1%
41 4
< 0.1%
ValueCountFrequency (%)
50094 1
 
< 0.1%
50089 4
 
< 0.1%
50085 1
 
< 0.1%
50080 9
 
< 0.1%
50078 1
 
< 0.1%
50074 64
< 0.1%
50072 9
 
< 0.1%
50071 1
 
< 0.1%
50070 1
 
< 0.1%
50063 1
 
< 0.1%

company_rating
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9844897
Minimum0
Maximum1
Zeros1745
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:58.389999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.92
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.068364596
Coefficient of variation (CV)0.069441657
Kurtosis113.81699
Mean0.9844897
Median Absolute Deviation (MAD)0
Skewness-9.4497057
Sum747827.24
Variance0.0046737181
MonotonicityNot monotonic
2022-11-24T14:50:58.574944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 611179
80.5%
0.99 44585
 
5.9%
0.97 16237
 
2.1%
0.93 12897
 
1.7%
0.98 12873
 
1.7%
0.96 8211
 
1.1%
0.94 7572
 
1.0%
0.9 6720
 
0.9%
0.95 6463
 
0.9%
0.89 6141
 
0.8%
Other values (54) 26731
 
3.5%
ValueCountFrequency (%)
0 1745
0.2%
0.1 13
 
< 0.1%
0.11 1
 
< 0.1%
0.13 3
 
< 0.1%
0.17 9
 
< 0.1%
0.2 23
 
< 0.1%
0.22 3
 
< 0.1%
0.25 96
 
< 0.1%
0.29 6
 
< 0.1%
0.3 36
 
< 0.1%
ValueCountFrequency (%)
1 611179
80.5%
0.99 44585
 
5.9%
0.98 12873
 
1.7%
0.97 16237
 
2.1%
0.96 8211
 
1.1%
0.95 6463
 
0.9%
0.94 7572
 
1.0%
0.93 12897
 
1.7%
0.92 2415
 
0.3%
0.91 3612
 
0.5%

company_location
Categorical

Distinct159
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Peru
383592 
Niger
 
35830
Isle of Man
 
31868
Barbados
 
27328
Nicaragua
 
20023
Other values (154)
260968 

Length

Max length51
Median length4
Mean length6.5122306
Min length4

Characters and Unicode

Total characters4946749
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
Peru 383592
50.5%
Niger 35830
 
4.7%
Isle of Man 31868
 
4.2%
Barbados 27328
 
3.6%
Nicaragua 20023
 
2.6%
Uzbekistan 19161
 
2.5%
Sao Tome and Principe 14834
 
2.0%
Croatia 14721
 
1.9%
Uganda 14529
 
1.9%
Zimbabwe 12553
 
1.7%
Other values (149) 185170
24.4%

Length

2022-11-24T14:50:58.768992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peru 383592
41.5%
niger 35830
 
3.9%
of 31881
 
3.5%
isle 31868
 
3.4%
man 31868
 
3.4%
barbados 27328
 
3.0%
and 23108
 
2.5%
nicaragua 20023
 
2.2%
uzbekistan 19161
 
2.1%
sao 14834
 
1.6%
Other values (206) 304526
33.0%

Most occurring characters

ValueCountFrequency (%)
e 632156
12.8%
r 583148
11.8%
a 511180
 
10.3%
u 463620
 
9.4%
P 409579
 
8.3%
i 281353
 
5.7%
n 249384
 
5.0%
o 189771
 
3.8%
s 171437
 
3.5%
164410
 
3.3%
Other values (47) 1290711
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3902388
78.9%
Uppercase Letter 868433
 
17.6%
Space Separator 164410
 
3.3%
Open Punctuation 5473
 
0.1%
Close Punctuation 5473
 
0.1%
Other Punctuation 545
 
< 0.1%
Decimal Number 26
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 632156
16.2%
r 583148
14.9%
a 511180
13.1%
u 463620
11.9%
i 281353
7.2%
n 249384
 
6.4%
o 189771
 
4.9%
s 171437
 
4.4%
d 121553
 
3.1%
l 108391
 
2.8%
Other values (16) 590395
15.1%
Uppercase Letter
ValueCountFrequency (%)
P 409579
47.2%
M 66117
 
7.6%
N 61882
 
7.1%
I 49516
 
5.7%
U 41113
 
4.7%
B 36452
 
4.2%
S 31674
 
3.6%
C 29706
 
3.4%
T 28882
 
3.3%
G 18377
 
2.1%
Other values (13) 95135
 
11.0%
Other Punctuation
ValueCountFrequency (%)
& 544
99.8%
' 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
6 13
50.0%
0 13
50.0%
Space Separator
ValueCountFrequency (%)
164410
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5473
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5473
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4770821
96.4%
Common 175928
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 632156
13.3%
r 583148
12.2%
a 511180
10.7%
u 463620
9.7%
P 409579
 
8.6%
i 281353
 
5.9%
n 249384
 
5.2%
o 189771
 
4.0%
s 171437
 
3.6%
d 121553
 
2.5%
Other values (39) 1157640
24.3%
Common
ValueCountFrequency (%)
164410
93.5%
( 5473
 
3.1%
) 5473
 
3.1%
& 544
 
0.3%
6 13
 
< 0.1%
0 13
 
< 0.1%
' 1
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4946749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 632156
12.8%
r 583148
11.8%
a 511180
 
10.3%
u 463620
 
9.4%
P 409579
 
8.3%
i 281353
 
5.7%
n 249384
 
5.0%
o 189771
 
3.8%
s 171437
 
3.5%
164410
 
3.3%
Other values (47) 1290711
26.1%

total_fleet_count
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean716.91878
Minimum1
Maximum1484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 MiB
2022-11-24T14:50:58.922688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q160
median1305
Q31305
95-th percentile1305
Maximum1484
Range1483
Interquartile range (IQR)1245

Descriptive statistics

Standard deviation616.04032
Coefficient of variation (CV)0.85928885
Kurtosis-1.9648316
Mean716.91878
Median Absolute Deviation (MAD)0
Skewness-0.08520072
Sum5.4457796 × 108
Variance379505.68
MonotonicityNot monotonic
2022-11-24T14:50:59.091635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1305 383358
50.5%
198 29808
 
3.9%
176 24288
 
3.2%
171 23881
 
3.1%
108 16846
 
2.2%
119 12996
 
1.7%
139 10625
 
1.4%
1484 9792
 
1.3%
2 9497
 
1.3%
1 9230
 
1.2%
Other values (71) 229288
30.2%
ValueCountFrequency (%)
1 9230
1.2%
2 9497
1.3%
3 6757
0.9%
4 5873
0.8%
5 4193
0.6%
6 4290
0.6%
7 3586
 
0.5%
8 3859
0.5%
9 3881
0.5%
10 3701
 
0.5%
ValueCountFrequency (%)
1484 9792
 
1.3%
1305 383358
50.5%
420 2376
 
0.3%
419 1
 
< 0.1%
198 29808
 
3.9%
185 48
 
< 0.1%
176 24288
 
3.2%
171 23881
 
3.1%
139 10625
 
1.4%
130 3476
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
False
621688 
True
137921 
ValueCountFrequency (%)
False 621688
81.8%
True 137921
 
18.2%
2022-11-24T14:50:59.307405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Interactions

2022-11-24T14:50:40.026769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:48:56.546728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:02.375290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:08.456978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:13.967680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:19.538821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:25.109459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:30.807591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:36.386980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:41.998621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:47.827429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-11-24T14:49:11.621101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:17.178491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:22.678100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:28.601715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:34.069563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:39.596221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:45.413682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:51.051516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:56.462489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:02.534021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:09.066506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:14.536266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:20.366277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:26.071833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:32.248652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:37.852780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:43.381752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:00.349426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:05.751890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:11.921768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:17.508302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:22.961250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:28.873331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:34.361137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:39.879423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:45.754148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:51.332448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:56.832317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:02.873424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:09.390507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:14.832814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:20.655735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:26.387214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:32.524674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:38.138008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:43.666959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:00.664657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:06.051333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:12.232315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:17.826171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:23.325492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:29.173682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:34.652161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:40.179960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:46.030925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:51.619358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:57.170904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:03.475539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:09.689764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:15.128813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:20.949550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:26.687198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:32.822726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:38.422788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:43.951912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:00.956485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:06.321769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:12.553579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:18.100078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:23.647241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:29.442757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:34.992401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:40.495916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:46.308539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:51.919754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:57.519569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:03.774149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:09.983913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:15.419393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:21.250197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:26.970874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:33.104321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:38.709135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:44.221581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:01.237172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:06.584587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:12.855190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:18.433592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:23.910074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:29.724406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:35.270550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:40.808755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:46.611955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:52.184177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:57.817607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:04.063294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:10.262719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:15.705141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:21.526195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:27.262822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:33.372315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:38.970785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:44.500332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:01.504031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:06.903544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:13.140180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:18.720951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:24.217727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:30.006395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:35.547119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:41.099855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:46.915926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:52.464568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:58.122820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:04.419134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:10.545619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:16.000712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:21.843127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:27.684673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:33.640381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:39.240430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:44.770000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:01.759926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:07.857697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:13.409604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:18.983989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:24.513602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:30.260223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:35.828198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:41.397598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:47.214272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:52.749951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:58.433679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:04.760302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:10.827448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:16.288813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:22.119630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:28.319417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:33.912380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:39.493915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:45.039536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:02.062572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:08.161901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:13.672365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:19.253550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:24.807671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:30.525796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:36.103781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:41.695219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:47.495625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:53.022491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:49:58.746948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:05.082252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:11.107313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:16.580853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:22.401018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:28.652079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:34.169662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T14:50:39.757089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-24T14:50:59.739882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-24T14:51:00.125029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-24T14:51:00.474640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-24T14:51:00.819440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-24T14:51:01.135721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-24T14:51:01.336012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-24T14:50:45.836253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-24T14:50:47.822323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_xshuttle_locationshuttle_typeengine_typeengine_vendorenginespassenger_capacitycancellation_policycrewd_check_completemoon_clearance_completepricecompany_idshuttle_idreview_scores_ratingreview_scores_comfortreview_scores_amenitiesreview_scores_tripreview_scores_crewreview_scores_locationreview_scores_pricenumber_of_reviewsreviews_per_monthid_ycompany_ratingcompany_locationtotal_fleet_countiata_approved
063561NiueType V5QuantumThetaBase Services1.02strict1.0FalseFalse1325.0350296356197.010.09.010.010.09.010.01331.65350291.0Niue4.0False
163561NiueType V5QuantumThetaBase Services1.02strict1.0FalseFalse1325.0350296356197.010.09.010.010.09.010.01331.65350291.0Niue4.0False
263561NiueType V5QuantumThetaBase Services1.02strict1.0FalseFalse1325.0350296356197.010.09.010.010.09.010.01331.65350291.0Niue4.0False
363561NiueType V5QuantumThetaBase Services1.02strict1.0FalseFalse1325.0350296356197.010.09.010.010.09.010.01331.65350291.0Niue4.0False
453260NiueType V5QuantumBanks, Wood and Phillips1.02strict1.0FalseFalse1325.0350295326098.010.09.010.010.09.010.0370.48350291.0Niue4.0False
553260NiueType V5QuantumBanks, Wood and Phillips1.02strict1.0FalseFalse1325.0350295326098.010.09.010.010.09.010.0370.48350291.0Niue4.0False
653260NiueType V5QuantumBanks, Wood and Phillips1.02strict1.0FalseFalse1325.0350295326098.010.09.010.010.09.010.0370.48350291.0Niue4.0False
753260NiueType V5QuantumBanks, Wood and Phillips1.02strict1.0FalseFalse1325.0350295326098.010.09.010.010.09.010.0370.48350291.0Niue4.0False
851019NiueType V5QuantumThetaBase Services1.02flexible1.0FalseFalse1260.0350295101992.010.09.010.010.09.09.0100.15350291.0Niue4.0False
951019NiueType V5QuantumThetaBase Services1.02flexible1.0FalseFalse1260.0350295101992.010.09.010.010.09.09.0100.15350291.0Niue4.0False
id_xshuttle_locationshuttle_typeengine_typeengine_vendorenginespassenger_capacitycancellation_policycrewd_check_completemoon_clearance_completepricecompany_idshuttle_idreview_scores_ratingreview_scores_comfortreview_scores_amenitiesreview_scores_tripreview_scores_crewreview_scores_locationreview_scores_pricenumber_of_reviewsreviews_per_monthid_ycompany_ratingcompany_locationtotal_fleet_countiata_approved
186394549839RwandaType V5NuclearBanks, Wood and Phillips1.01flexible1.0TrueFalse1195.0287849839100.010.010.010.010.010.010.022.028780.93Bosnia and Herzegovina3.0True
186394649839RwandaType V5NuclearBanks, Wood and Phillips1.01flexible1.0TrueFalse1195.0287849839100.010.010.010.010.010.010.022.028780.93Bosnia and Herzegovina3.0True
186394749839RwandaType V5NuclearBanks, Wood and Phillips1.01flexible1.0TrueFalse1195.0287849839100.010.010.010.010.010.010.022.028780.93Bosnia and Herzegovina3.0True
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Duplicate rows

Most frequently occurring

id_xshuttle_locationshuttle_typeengine_typeengine_vendorenginespassenger_capacitycancellation_policycrewd_check_completemoon_clearance_completepricecompany_idshuttle_idreview_scores_ratingreview_scores_comfortreview_scores_amenitiesreview_scores_tripreview_scores_crewreview_scores_locationreview_scores_pricenumber_of_reviewsreviews_per_monthid_ycompany_ratingcompany_locationtotal_fleet_countiata_approved# duplicates
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