Overview

Dataset statistics

Number of variables4
Number of observations11783870
Missing cells0
Missing cells (%)0.0%
Total size in memory668.8 MiB
Average record size in memory59.5 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 "wenetItaly"Constant

Reproduction

Analysis started2024-11-23 06:05:46.113672
Analysis finished2024-11-23 06:06:30.942561
Duration44.83 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 size292.2 MiB
2024-11-23T07:06:31.025786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters117838700
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 rowwenetItaly
2nd rowwenetItaly
3rd rowwenetItaly
4th rowwenetItaly
5th rowwenetItaly
ValueCountFrequency (%)
wenetitaly 11783870
100.0%
2024-11-23T07:06:31.279603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 23567740
20.0%
t 23567740
20.0%
w 11783870
10.0%
n 11783870
10.0%
I 11783870
10.0%
a 11783870
10.0%
l 11783870
10.0%
y 11783870
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117838700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 23567740
20.0%
t 23567740
20.0%
w 11783870
10.0%
n 11783870
10.0%
I 11783870
10.0%
a 11783870
10.0%
l 11783870
10.0%
y 11783870
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117838700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 23567740
20.0%
t 23567740
20.0%
w 11783870
10.0%
n 11783870
10.0%
I 11783870
10.0%
a 11783870
10.0%
l 11783870
10.0%
y 11783870
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117838700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 23567740
20.0%
t 23567740
20.0%
w 11783870
10.0%
n 11783870
10.0%
I 11783870
10.0%
a 11783870
10.0%
l 11783870
10.0%
y 11783870
10.0%

userid
Real number (ℝ)

User id

Distinct221
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.0191999
Minimum0
Maximum265
Zeros5954
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size179.8 MiB
2024-11-23T07:06:31.407493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q158
median118
Q3204
95-th percentile255
Maximum265
Range265
Interquartile range (IQR)146

Descriptive statistics

Standard deviation79.41900029
Coefficient of variation (CV)0.6015715923
Kurtosis-1.245445939
Mean132.0191999
Median Absolute Deviation (MAD)72
Skewness0.07454661916
Sum1555697089
Variance6307.377607
MonotonicityIncreasing
2024-11-23T07:06:31.533477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 543543
 
4.6%
57 376437
 
3.2%
118 322618
 
2.7%
217 293832
 
2.5%
175 284141
 
2.4%
258 282134
 
2.4%
245 270271
 
2.3%
103 255287
 
2.2%
18 229836
 
2.0%
112 228135
 
1.9%
Other values (211) 8697636
73.8%
ValueCountFrequency (%)
0 5954
 
0.1%
1 55829
0.5%
2 49090
0.4%
3 43157
0.4%
4 13506
 
0.1%
ValueCountFrequency (%)
265 69054
0.6%
264 818
 
< 0.1%
263 36198
0.3%
262 23941
 
0.2%
260 352
 
< 0.1%

timestamp
Date

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

Distinct11740109
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size179.8 MiB
Minimum2020-11-16 07:00:00.202000
Maximum2020-12-11 21:59:58.764000
2024-11-23T07:06:31.653584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-23T07:06:31.774971image/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 size286.7 MiB
2024-11-23T07:06:31.853179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.512468824
Min length9

Characters and Unicode

Total characters112093696
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_OFF
2nd rowSCREEN_ON
3rd rowSCREEN_OFF
4th rowSCREEN_ON
5th rowSCREEN_OFF
ValueCountFrequency (%)
screen_off 6038866
51.2%
screen_on 5745004
48.8%
2024-11-23T07:06:32.036403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 23567740
21.0%
N 17528874
15.6%
F 12077732
10.8%
S 11783870
10.5%
C 11783870
10.5%
R 11783870
10.5%
_ 11783870
10.5%
O 11783870
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112093696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 23567740
21.0%
N 17528874
15.6%
F 12077732
10.8%
S 11783870
10.5%
C 11783870
10.5%
R 11783870
10.5%
_ 11783870
10.5%
O 11783870
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112093696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 23567740
21.0%
N 17528874
15.6%
F 12077732
10.8%
S 11783870
10.5%
C 11783870
10.5%
R 11783870
10.5%
_ 11783870
10.5%
O 11783870
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112093696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 23567740
21.0%
N 17528874
15.6%
F 12077732
10.8%
S 11783870
10.5%
C 11783870
10.5%
R 11783870
10.5%
_ 11783870
10.5%
O 11783870
10.5%