Overview

Dataset statistics

Number of variables13
Number of observations128284
Missing cells621209
Missing cells (%)37.2%
Total size in memory14.9 MiB
Average record size in memory122.0 B

Variable types

Text2
Numeric10
DateTime1

Dataset

Description[unitless] Sensor that returns a label identifying the activity performed by the user, accurately detected using low power signals from multiple sensors in the device. This is achieved using Google’s Activity Recognition APIs. Possible activities are: still, in_vehicle, on_bycicle, on_foot, running, tilting, walking. To compare each sensor observation, the frequency was reduced to one minute. The first non-missing name is reported for each of the categorical variables.
CreatorAndrea Bontempelli, Matteo Busso, Roy Alia Asiku
AuthorAndrea Bontempelli, Matteo Busso, Fausto Giunchiglia
URL
Copyright(c) University of Trento - Knowledge Diversity 2023

Variable descriptions

experimentidExperiment Id
useridUser id
timestampshow month(2), day(2), hour(2), minute(2), second(2), decimals(3)
accuracyThe highest accuracy for possible activities
labelThe activity name with highest accuracy
StillThe value of the "still" activity
TiltingThe value of the "tilting" activity
InVehicleThe value of the "in_vehicle" activity
OnBicycleThe value of the "on_bicycle" activity
OnFootThe value of the "on_foot" activity
UnknownThe value of the "unknown" activity
WalkingThe value of the "walking" activity
RunningThe value of the "running" activity

Alerts

experimentid has constant value "wenetDenmark"Constant
Tilting has constant value "100.0"Constant
InVehicle is highly overall correlated with Running and 1 other fieldsHigh correlation
OnBicycle is highly overall correlated with RunningHigh correlation
OnFoot is highly overall correlated with Running and 1 other fieldsHigh correlation
Running is highly overall correlated with InVehicle and 5 other fieldsHigh correlation
Still is highly overall correlated with Unknown and 1 other fieldsHigh correlation
Unknown is highly overall correlated with Running and 2 other fieldsHigh correlation
Walking is highly overall correlated with OnFoot and 1 other fieldsHigh correlation
accuracy is highly overall correlated with InVehicle and 3 other fieldsHigh correlation
Still has 29052 (22.6%) missing valuesMissing
Tilting has 112453 (87.7%) missing valuesMissing
InVehicle has 81063 (63.2%) missing valuesMissing
OnBicycle has 83446 (65.0%) missing valuesMissing
OnFoot has 77905 (60.7%) missing valuesMissing
Unknown has 61389 (47.9%) missing valuesMissing
Walking has 77930 (60.7%) missing valuesMissing
Running has 97971 (76.4%) missing valuesMissing
userid has 9013 (7.0%) zerosZeros

Reproduction

Analysis started2024-11-23 02:05:10.235664
Analysis finished2024-11-23 02:05:11.767074
Duration1.53 second
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

experimentid
Text

CONSTANT 

Experiment Id

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-11-23T03:05:11.822106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1539408
Distinct characters9
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 rowwenetDenmark
2nd rowwenetDenmark
3rd rowwenetDenmark
4th rowwenetDenmark
5th rowwenetDenmark
ValueCountFrequency (%)
wenetdenmark 128284
100.0%
2024-11-23T03:05:12.028892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 384852
25.0%
n 256568
16.7%
w 128284
 
8.3%
t 128284
 
8.3%
D 128284
 
8.3%
m 128284
 
8.3%
a 128284
 
8.3%
r 128284
 
8.3%
k 128284
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1539408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 384852
25.0%
n 256568
16.7%
w 128284
 
8.3%
t 128284
 
8.3%
D 128284
 
8.3%
m 128284
 
8.3%
a 128284
 
8.3%
r 128284
 
8.3%
k 128284
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1539408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 384852
25.0%
n 256568
16.7%
w 128284
 
8.3%
t 128284
 
8.3%
D 128284
 
8.3%
m 128284
 
8.3%
a 128284
 
8.3%
r 128284
 
8.3%
k 128284
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1539408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 384852
25.0%
n 256568
16.7%
w 128284
 
8.3%
t 128284
 
8.3%
D 128284
 
8.3%
m 128284
 
8.3%
a 128284
 
8.3%
r 128284
 
8.3%
k 128284
 
8.3%

userid
Real number (ℝ)

ZEROS 

User id

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.48076923
Minimum0
Maximum27
Zeros9013
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:12.133938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median17
Q321
95-th percentile26
Maximum27
Range27
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.040853449
Coefficient of variation (CV)0.6706481873
Kurtosis-1.46560377
Mean13.48076923
Median Absolute Deviation (MAD)9
Skewness-0.1862010621
Sum1729367
Variance81.73703108
MonotonicityIncreasing
2024-11-23T03:05:12.330080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
17 28835
22.5%
2 23030
18.0%
6 14017
10.9%
26 11638
9.1%
22 11200
 
8.7%
0 9013
 
7.0%
20 8769
 
6.8%
21 7534
 
5.9%
12 5188
 
4.0%
27 3354
 
2.6%
Other values (4) 5706
 
4.4%
ValueCountFrequency (%)
0 9013
 
7.0%
2 23030
18.0%
3 2710
 
2.1%
6 14017
10.9%
8 390
 
0.3%
ValueCountFrequency (%)
27 3354
 
2.6%
26 11638
9.1%
25 2208
 
1.7%
22 11200
8.7%
21 7534
5.9%

timestamp
Date

show month(2), day(2), hour(2), minute(2), second(2), decimals(3)

Distinct128282
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1002.3 KiB
Minimum2020-11-16 07:00:47.064000
Maximum2020-12-11 21:59:26.016000
2024-11-23T03:05:12.441082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-23T03:05:12.558317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

accuracy
Real number (ℝ)

HIGH CORRELATION 

The highest accuracy for possible activities

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.6501902
Minimum23
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:12.673880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile40
Q178
median99
Q3100
95-th percentile100
Maximum100
Range77
Interquartile range (IQR)22

Descriptive statistics

Standard deviation24.35483102
Coefficient of variation (CV)0.2877114743
Kurtosis-0.4538341368
Mean84.6501902
Median Absolute Deviation (MAD)1
Skewness-1.19378198
Sum10859265
Variance593.157794
MonotonicityNot monotonic
2024-11-23T03:05:12.803468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 59886
46.7%
40 22922
 
17.9%
99 10392
 
8.1%
97 6279
 
4.9%
96 5664
 
4.4%
98 5477
 
4.3%
95 857
 
0.7%
94 776
 
0.6%
93 741
 
0.6%
86 680
 
0.5%
Other values (68) 14610
 
11.4%
ValueCountFrequency (%)
23 3
 
< 0.1%
24 1
 
< 0.1%
25 7
 
< 0.1%
26 27
< 0.1%
27 40
< 0.1%
ValueCountFrequency (%)
100 59886
46.7%
99 10392
 
8.1%
98 5477
 
4.3%
97 6279
 
4.9%
96 5664
 
4.4%

label
Text

The activity name with highest accuracy

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-11-23T03:05:12.892158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.969060834
Min length5

Characters and Unicode

Total characters765735
Distinct characters20
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 rowStill
2nd rowStill
3rd rowStill
4th rowStill
5th rowStill
ValueCountFrequency (%)
still 69626
54.3%
unknown 22700
 
17.7%
tilting 15831
 
12.3%
onfoot 11085
 
8.6%
onbicycle 6573
 
5.1%
invehicle 2469
 
1.9%
2024-11-23T03:05:13.094074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 164125
21.4%
i 110330
14.4%
n 104058
13.6%
t 96542
12.6%
S 69626
9.1%
o 44870
 
5.9%
U 22700
 
3.0%
k 22700
 
3.0%
w 22700
 
3.0%
O 17658
 
2.3%
Other values (10) 90426
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 765735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 164125
21.4%
i 110330
14.4%
n 104058
13.6%
t 96542
12.6%
S 69626
9.1%
o 44870
 
5.9%
U 22700
 
3.0%
k 22700
 
3.0%
w 22700
 
3.0%
O 17658
 
2.3%
Other values (10) 90426
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 765735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 164125
21.4%
i 110330
14.4%
n 104058
13.6%
t 96542
12.6%
S 69626
9.1%
o 44870
 
5.9%
U 22700
 
3.0%
k 22700
 
3.0%
w 22700
 
3.0%
O 17658
 
2.3%
Other values (10) 90426
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 765735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 164125
21.4%
i 110330
14.4%
n 104058
13.6%
t 96542
12.6%
S 69626
9.1%
o 44870
 
5.9%
U 22700
 
3.0%
k 22700
 
3.0%
w 22700
 
3.0%
O 17658
 
2.3%
Other values (10) 90426
11.8%

Still
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "still" activity

Distinct100
Distinct (%)0.1%
Missing29052
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean67.76110529
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:13.222292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median99
Q3100
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)90

Descriptive statistics

Standard deviation41.34161378
Coefficient of variation (CV)0.6101083151
Kurtosis-1.467630136
Mean67.76110529
Median Absolute Deviation (MAD)1
Skewness-0.6441113905
Sum6724070
Variance1709.12903
MonotonicityNot monotonic
2024-11-23T03:05:13.344388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 41751
32.5%
10 22740
17.7%
99 9183
 
7.2%
1 3873
 
3.0%
98 3140
 
2.4%
97 2947
 
2.3%
96 2500
 
1.9%
2 1203
 
0.9%
44 629
 
0.5%
3 606
 
0.5%
Other values (90) 10660
 
8.3%
(Missing) 29052
22.6%
ValueCountFrequency (%)
1 3873
3.0%
2 1203
 
0.9%
3 606
 
0.5%
4 400
 
0.3%
5 280
 
0.2%
ValueCountFrequency (%)
100 41751
32.5%
99 9183
 
7.2%
98 3140
 
2.4%
97 2947
 
2.3%
96 2500
 
1.9%

Tilting
Real number (ℝ)

CONSTANT  MISSING 

The value of the "tilting" activity

Distinct1
Distinct (%)< 0.1%
Missing112453
Missing (%)87.7%
Infinite0
Infinite (%)0.0%
Mean100
Minimum100
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:13.436632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean100
Median Absolute Deviation (MAD)0
Skewness0
Sum1583100
Variance0
MonotonicityIncreasing
2024-11-23T03:05:13.517802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
100 15831
 
12.3%
(Missing) 112453
87.7%
ValueCountFrequency (%)
100 15831
12.3%
ValueCountFrequency (%)
100 15831
12.3%

InVehicle
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "in_vehicle" activity

Distinct77
Distinct (%)0.2%
Missing81063
Missing (%)63.2%
Infinite0
Infinite (%)0.0%
Mean13.24573812
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:13.623991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q310
95-th percentile74
Maximum100
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation19.22460506
Coefficient of variation (CV)1.451380427
Kurtosis11.15252251
Mean13.24573812
Median Absolute Deviation (MAD)1
Skewness3.402803302
Sum625477
Variance369.5854395
MonotonicityNot monotonic
2024-11-23T03:05:13.743148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 23094
 
18.0%
1 7875
 
6.1%
2 2835
 
2.2%
3 1100
 
0.9%
4 731
 
0.6%
23 653
 
0.5%
5 638
 
0.5%
6 549
 
0.4%
7 486
 
0.4%
96 469
 
0.4%
Other values (67) 8791
 
6.9%
(Missing) 81063
63.2%
ValueCountFrequency (%)
1 7875
6.1%
2 2835
 
2.2%
3 1100
 
0.9%
4 731
 
0.6%
5 638
 
0.5%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 11
 
< 0.1%
98 102
 
0.1%
97 340
0.3%
96 469
0.4%

OnBicycle
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "on_bicycle" activity

Distinct79
Distinct (%)0.2%
Missing83446
Missing (%)65.0%
Infinite0
Infinite (%)0.0%
Mean20.36114456
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:13.862993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q310
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)6

Descriptive statistics

Standard deviation31.42601288
Coefficient of variation (CV)1.543430566
Kurtosis2.061353296
Mean20.36114456
Median Absolute Deviation (MAD)0
Skewness1.975273958
Sum912953
Variance987.5942857
MonotonicityNot monotonic
2024-11-23T03:05:13.984724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 22932
 
17.9%
1 4809
 
3.7%
2 3244
 
2.5%
100 2303
 
1.8%
3 2006
 
1.6%
4 1391
 
1.1%
99 1104
 
0.9%
5 1042
 
0.8%
97 801
 
0.6%
6 738
 
0.6%
Other values (69) 4468
 
3.5%
(Missing) 83446
65.0%
ValueCountFrequency (%)
1 4809
3.7%
2 3244
2.5%
3 2006
1.6%
4 1391
 
1.1%
5 1042
 
0.8%
ValueCountFrequency (%)
100 2303
1.8%
99 1104
0.9%
98 611
 
0.5%
97 801
 
0.6%
96 350
 
0.3%

OnFoot
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "on_foot" activity

Distinct93
Distinct (%)0.2%
Missing77905
Missing (%)60.7%
Infinite0
Infinite (%)0.0%
Mean28.44026281
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:14.103649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median10
Q327
95-th percentile97
Maximum99
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation35.22152097
Coefficient of variation (CV)1.23843866
Kurtosis-0.2361998542
Mean28.44026281
Median Absolute Deviation (MAD)2
Skewness1.274789557
Sum1432792
Variance1240.55554
MonotonicityNot monotonic
2024-11-23T03:05:14.225170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 23147
 
18.0%
1 4789
 
3.7%
96 2345
 
1.8%
97 2191
 
1.7%
98 1624
 
1.3%
2 1236
 
1.0%
11 754
 
0.6%
3 658
 
0.5%
8 614
 
0.5%
5 611
 
0.5%
Other values (83) 12410
 
9.7%
(Missing) 77905
60.7%
ValueCountFrequency (%)
1 4789
3.7%
2 1236
 
1.0%
3 658
 
0.5%
4 504
 
0.4%
5 611
 
0.5%
ValueCountFrequency (%)
99 94
 
0.1%
98 1624
1.3%
97 2191
1.7%
96 2345
1.8%
95 540
 
0.4%

Unknown
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "unknown" activity

Distinct5
Distinct (%)< 0.1%
Missing61389
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean14.63619105
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:14.318062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q340
95-th percentile40
Maximum40
Range39
Interquartile range (IQR)39

Descriptive statistics

Standard deviation18.18627311
Coefficient of variation (CV)1.242555051
Kurtosis-1.53939807
Mean14.63619105
Median Absolute Deviation (MAD)1
Skewness0.675767762
Sum979088
Variance330.7405295
MonotonicityNot monotonic
2024-11-23T03:05:14.412915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
40 22700
 
17.7%
1 22069
 
17.2%
2 17493
 
13.6%
3 4499
 
3.5%
4 134
 
0.1%
(Missing) 61389
47.9%
ValueCountFrequency (%)
1 22069
17.2%
2 17493
13.6%
3 4499
 
3.5%
4 134
 
0.1%
40 22700
17.7%
ValueCountFrequency (%)
40 22700
17.7%
4 134
 
0.1%
3 4499
 
3.5%
2 17493
13.6%
1 22069
17.2%

Walking
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "walking" activity

Distinct66
Distinct (%)0.1%
Missing77930
Missing (%)60.7%
Infinite0
Infinite (%)0.0%
Mean28.37859952
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:14.528682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median10
Q327
95-th percentile97
Maximum99
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation35.18690795
Coefficient of variation (CV)1.239909952
Kurtosis-0.2199809557
Mean28.37859952
Median Absolute Deviation (MAD)2
Skewness1.281243991
Sum1428976
Variance1238.118491
MonotonicityNot monotonic
2024-11-23T03:05:14.767617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 23150
 
18.0%
1 4786
 
3.7%
96 2345
 
1.8%
97 2189
 
1.7%
98 1623
 
1.3%
2 1232
 
1.0%
11 755
 
0.6%
3 648
 
0.5%
8 615
 
0.5%
5 611
 
0.5%
Other values (56) 12400
 
9.7%
(Missing) 77930
60.7%
ValueCountFrequency (%)
1 4786
3.7%
2 1232
 
1.0%
3 648
 
0.5%
4 504
 
0.4%
5 611
 
0.5%
ValueCountFrequency (%)
99 94
 
0.1%
98 1623
1.3%
97 2189
1.7%
96 2345
1.8%
95 540
 
0.4%

Running
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "running" activity

Distinct61
Distinct (%)0.2%
Missing97971
Missing (%)76.4%
Infinite0
Infinite (%)0.0%
Mean8.125457724
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1002.3 KiB
2024-11-23T03:05:14.887961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median10
Q310
95-th percentile10
Maximum98
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.935904445
Coefficient of variation (CV)0.6074617101
Kurtosis80.36715228
Mean8.125457724
Median Absolute Deviation (MAD)0
Skewness5.405023498
Sum246307
Variance24.36315269
MonotonicityNot monotonic
2024-11-23T03:05:15.010996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 22715
 
17.7%
1 4812
 
3.8%
2 1505
 
1.2%
3 578
 
0.5%
4 259
 
0.2%
5 140
 
0.1%
6 81
 
0.1%
7 55
 
< 0.1%
8 29
 
< 0.1%
9 23
 
< 0.1%
Other values (51) 116
 
0.1%
(Missing) 97971
76.4%
ValueCountFrequency (%)
1 4812
3.8%
2 1505
 
1.2%
3 578
 
0.5%
4 259
 
0.2%
5 140
 
0.1%
ValueCountFrequency (%)
98 1
< 0.1%
97 2
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%

Correlations

2024-11-23T03:05:15.090531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
InVehicleOnBicycleOnFootRunningStillUnknownWalkingaccuracyuserid
InVehicle1.000-0.100-0.1450.518-0.2280.235-0.145-0.5070.012
OnBicycle-0.1001.000-0.4870.600-0.2920.373-0.486-0.058-0.015
OnFoot-0.145-0.4871.000-0.884-0.485-0.1290.9990.1740.070
Running0.5180.600-0.8841.0000.2680.949-0.895-0.727-0.039
Still-0.228-0.292-0.4850.2681.000-0.645-0.4850.933-0.134
Unknown0.2350.373-0.1290.949-0.6451.000-0.129-0.8100.047
Walking-0.145-0.4860.999-0.895-0.485-0.1291.0000.1750.070
accuracy-0.507-0.0580.174-0.7270.933-0.8100.1751.000-0.117
userid0.012-0.0150.070-0.039-0.1340.0470.070-0.1171.000