Overview

Dataset statistics

Number of variables4
Number of observations1821139
Missing cells799476
Missing cells (%)11.0%
Total size in memory93.4 MiB
Average record size in memory53.8 B

Variable types

Text2
Numeric1
DateTime1

Dataset

Description[unitless] Returns the name of the application (or application package) that is currently running in the foreground of the phone. 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)
applicationnameName of the application

Alerts

experimentid has constant value "wenetIndia"Constant
applicationname has 799476 (43.9%) missing valuesMissing
userid has 404216 (22.2%) zerosZeros

Reproduction

Analysis started2024-11-24 09:13:58.999286
Analysis finished2024-11-24 09:14:05.202850
Duration6.2 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

experimentid
Text

CONSTANT 

Experiment Id

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-11-24T10:14:05.268147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters18211390
Distinct characters8
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 rowwenetIndia
2nd rowwenetIndia
3rd rowwenetIndia
4th rowwenetIndia
5th rowwenetIndia
ValueCountFrequency (%)
wenetindia 1821139
100.0%
2024-11-24T10:14:05.528324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3642278
20.0%
n 3642278
20.0%
w 1821139
10.0%
t 1821139
10.0%
I 1821139
10.0%
d 1821139
10.0%
i 1821139
10.0%
a 1821139
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18211390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3642278
20.0%
n 3642278
20.0%
w 1821139
10.0%
t 1821139
10.0%
I 1821139
10.0%
d 1821139
10.0%
i 1821139
10.0%
a 1821139
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18211390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3642278
20.0%
n 3642278
20.0%
w 1821139
10.0%
t 1821139
10.0%
I 1821139
10.0%
d 1821139
10.0%
i 1821139
10.0%
a 1821139
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18211390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3642278
20.0%
n 3642278
20.0%
w 1821139
10.0%
t 1821139
10.0%
I 1821139
10.0%
d 1821139
10.0%
i 1821139
10.0%
a 1821139
10.0%

userid
Real number (ℝ)

ZEROS 

User id

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.25934868
Minimum0
Maximum62
Zeros404216
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size13.9 MiB
2024-11-24T10:14:05.632101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median12
Q318
95-th percentile49
Maximum62
Range62
Interquartile range (IQR)9

Descriptive statistics

Standard deviation16.6051239
Coefficient of variation (CV)0.9620944685
Kurtosis0.07127638603
Mean17.25934868
Median Absolute Deviation (MAD)6
Skewness1.082105337
Sum31431673
Variance275.7301396
MonotonicityIncreasing
2024-11-24T10:14:05.726110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
12 421788
23.2%
0 404216
22.2%
9 237458
13.0%
17 183528
10.1%
43 177111
9.7%
18 130058
 
7.1%
49 63740
 
3.5%
57 59261
 
3.3%
44 34495
 
1.9%
8 19606
 
1.1%
Other values (10) 89878
 
4.9%
ValueCountFrequency (%)
0 404216
22.2%
4 13512
 
0.7%
8 19606
 
1.1%
9 237458
13.0%
12 421788
23.2%
ValueCountFrequency (%)
62 18533
 
1.0%
57 59261
3.3%
49 63740
3.5%
46 2535
 
0.1%
44 34495
1.9%

timestamp
Date

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

Distinct1820533
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size13.9 MiB
Minimum2021-07-12 08:00:02.558000
Maximum2021-08-12 14:41:44.912000
2024-11-24T10:14:05.840243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-24T10:14:05.969857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

applicationname
Text

MISSING 

Name of the application

Distinct251
Distinct (%)< 0.1%
Missing799476
Missing (%)43.9%
Memory size34.3 MiB
2024-11-24T10:14:06.103086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length44
Median length41
Mean length20.76038968
Min length7

Characters and Unicode

Total characters21210122
Distinct characters47
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

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowapp.nekko.to
2nd rowapp.nekko.to
3rd rowapp.nekko.to
4th rowapp.nekko.to
5th rowapp.nekko.to
ValueCountFrequency (%)
net.oneplus.launcher 152760
15.0%
com.lge.launcher3 126924
12.4%
com.sec.android.app.launcher 116149
 
11.4%
com.instagram.android 72153
 
7.1%
com.whatsapp 58285
 
5.7%
com.android.chrome 51910
 
5.1%
com.android.launcher3 44150
 
4.3%
com.microsoft.teams 38072
 
3.7%
com.dta.trigon 30125
 
2.9%
com.microsoft.skydrive 22396
 
2.2%
Other values (241) 308739
30.2%
2024-11-24T10:14:06.394111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2348504
11.1%
o 1979407
 
9.3%
c 1802568
 
8.5%
a 1648475
 
7.8%
e 1607946
 
7.6%
n 1517427
 
7.2%
r 1319848
 
6.2%
m 1184997
 
5.6%
l 1071868
 
5.1%
d 909905
 
4.3%
Other values (37) 5819177
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21210122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2348504
11.1%
o 1979407
 
9.3%
c 1802568
 
8.5%
a 1648475
 
7.8%
e 1607946
 
7.6%
n 1517427
 
7.2%
r 1319848
 
6.2%
m 1184997
 
5.6%
l 1071868
 
5.1%
d 909905
 
4.3%
Other values (37) 5819177
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21210122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2348504
11.1%
o 1979407
 
9.3%
c 1802568
 
8.5%
a 1648475
 
7.8%
e 1607946
 
7.6%
n 1517427
 
7.2%
r 1319848
 
6.2%
m 1184997
 
5.6%
l 1071868
 
5.1%
d 909905
 
4.3%
Other values (37) 5819177
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21210122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2348504
11.1%
o 1979407
 
9.3%
c 1802568
 
8.5%
a 1648475
 
7.8%
e 1607946
 
7.6%
n 1517427
 
7.2%
r 1319848
 
6.2%
m 1184997
 
5.6%
l 1071868
 
5.1%
d 909905
 
4.3%
Other values (37) 5819177
27.4%