Overview

Dataset statistics

Number of variables4
Number of observations976406
Missing cells0
Missing cells (%)0.0%
Total size in memory49.8 MiB
Average record size in memory53.5 B

Variable types

Text2
Numeric1
DateTime1

Dataset

Description[0/1] Returns whether the phone's screen is on or off. 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)
statusReturn if the screen is ON

Alerts

experimentid has constant value "wenetDenmark"Constant
userid has 30308 (3.1%) zerosZeros

Reproduction

Analysis started2024-11-23 01:51:38.450097
Analysis finished2024-11-23 01:51:41.992818
Duration3.54 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 size18.6 MiB
2024-11-23T02:51:42.073997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters11716872
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 976406
100.0%
2024-11-23T02:51:42.326415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2929218
25.0%
n 1952812
16.7%
w 976406
 
8.3%
t 976406
 
8.3%
D 976406
 
8.3%
m 976406
 
8.3%
a 976406
 
8.3%
r 976406
 
8.3%
k 976406
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11716872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2929218
25.0%
n 1952812
16.7%
w 976406
 
8.3%
t 976406
 
8.3%
D 976406
 
8.3%
m 976406
 
8.3%
a 976406
 
8.3%
r 976406
 
8.3%
k 976406
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11716872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2929218
25.0%
n 1952812
16.7%
w 976406
 
8.3%
t 976406
 
8.3%
D 976406
 
8.3%
m 976406
 
8.3%
a 976406
 
8.3%
r 976406
 
8.3%
k 976406
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11716872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2929218
25.0%
n 1952812
16.7%
w 976406
 
8.3%
t 976406
 
8.3%
D 976406
 
8.3%
m 976406
 
8.3%
a 976406
 
8.3%
r 976406
 
8.3%
k 976406
 
8.3%

userid
Real number (ℝ)

ZEROS 

User id

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5316692
Minimum0
Maximum27
Zeros30308
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-11-23T02:51:42.432699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median17
Q317
95-th percentile23
Maximum27
Range27
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.940934461
Coefficient of variation (CV)0.6336693327
Kurtosis-1.480140078
Mean12.5316692
Median Absolute Deviation (MAD)6
Skewness-0.002721324503
Sum12235997
Variance63.05844012
MonotonicityIncreasing
2024-11-23T02:51:42.531956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17 333044
34.1%
6 214329
22.0%
3 172635
17.7%
23 113514
 
11.6%
21 30695
 
3.1%
0 30308
 
3.1%
2 18596
 
1.9%
26 16361
 
1.7%
25 15076
 
1.5%
27 10871
 
1.1%
Other values (7) 20977
 
2.1%
ValueCountFrequency (%)
0 30308
 
3.1%
2 18596
 
1.9%
3 172635
17.7%
6 214329
22.0%
8 1174
 
0.1%
ValueCountFrequency (%)
27 10871
 
1.1%
26 16361
 
1.7%
25 15076
 
1.5%
23 113514
11.6%
22 7221
 
0.7%

timestamp
Date

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

Distinct976066
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
Minimum2020-11-16 07:00:00.479000
Maximum2020-12-11 21:58:41.435000
2024-11-23T02:51:42.750706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-23T02:51:42.873461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

status
Text

Return if the screen is ON

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.3 MiB
2024-11-23T02:51:42.952291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.503232262
Min length9

Characters and Unicode

Total characters9279013
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 rowSCREEN_ON
2nd rowSCREEN_ON
3rd rowSCREEN_ON
4th rowSCREEN_OFF
5th rowSCREEN_OFF
ValueCountFrequency (%)
screen_off 491359
50.3%
screen_on 485047
49.7%
2024-11-23T02:51:43.133485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1952812
21.0%
N 1461453
15.8%
F 982718
10.6%
S 976406
10.5%
C 976406
10.5%
R 976406
10.5%
_ 976406
10.5%
O 976406
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9279013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1952812
21.0%
N 1461453
15.8%
F 982718
10.6%
S 976406
10.5%
C 976406
10.5%
R 976406
10.5%
_ 976406
10.5%
O 976406
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9279013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1952812
21.0%
N 1461453
15.8%
F 982718
10.6%
S 976406
10.5%
C 976406
10.5%
R 976406
10.5%
_ 976406
10.5%
O 976406
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9279013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1952812
21.0%
N 1461453
15.8%
F 982718
10.6%
S 976406
10.5%
C 976406
10.5%
R 976406
10.5%
_ 976406
10.5%
O 976406
10.5%