Overview

Dataset statistics

Number of variables6
Number of observations120267
Missing cells0
Missing cells (%)0.0%
Total size in memory14.4 MiB
Average record size in memory125.2 B

Variable types

Text3
Numeric2
DateTime1

Dataset

Description[unitless] Sensor that periodically collects information about the cellular networks (name, id, type) the smartphone is connected to. 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)
cellidThe cell id
dbm(DeciBel-Milliwatts)The received signal strength.
typeThe technology type of the network (lte, wcdma, gsm, etc…)

Alerts

experimentid has constant value "wenetIndia"Constant
timestamp has unique valuesUnique
userid has 45020 (37.4%) zerosZeros

Reproduction

Analysis started2024-11-22 13:01:49.958108
Analysis finished2024-11-22 13:01:50.753526
Duration0.8 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 size2.1 MiB
2024-11-22T14:01:50.848491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1202670
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 120267
100.0%
2024-11-22T14:01:51.077416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 240534
20.0%
n 240534
20.0%
w 120267
10.0%
t 120267
10.0%
I 120267
10.0%
d 120267
10.0%
i 120267
10.0%
a 120267
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1202670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 240534
20.0%
n 240534
20.0%
w 120267
10.0%
t 120267
10.0%
I 120267
10.0%
d 120267
10.0%
i 120267
10.0%
a 120267
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1202670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 240534
20.0%
n 240534
20.0%
w 120267
10.0%
t 120267
10.0%
I 120267
10.0%
d 120267
10.0%
i 120267
10.0%
a 120267
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1202670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 240534
20.0%
n 240534
20.0%
w 120267
10.0%
t 120267
10.0%
I 120267
10.0%
d 120267
10.0%
i 120267
10.0%
a 120267
10.0%

userid
Real number (ℝ)

ZEROS 

User id

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.91251964
Minimum0
Maximum57
Zeros45020
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size939.7 KiB
2024-11-22T14:01:51.190388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q317
95-th percentile49
Maximum57
Range57
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.45893869
Coefficient of variation (CV)1.197205435
Kurtosis1.183286836
Mean12.91251964
Median Absolute Deviation (MAD)9
Skewness1.430232064
Sum1552950
Variance238.9787855
MonotonicityIncreasing
2024-11-22T14:01:51.281917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 45020
37.4%
9 27414
22.8%
17 25776
21.4%
43 7515
 
6.2%
57 4101
 
3.4%
49 2342
 
1.9%
8 1894
 
1.6%
12 1592
 
1.3%
44 1490
 
1.2%
35 1411
 
1.2%
Other values (4) 1712
 
1.4%
ValueCountFrequency (%)
0 45020
37.4%
8 1894
 
1.6%
9 27414
22.8%
12 1592
 
1.3%
17 25776
21.4%
ValueCountFrequency (%)
57 4101
3.4%
49 2342
 
1.9%
44 1490
 
1.2%
43 7515
6.2%
40 94
 
0.1%

timestamp
Date

UNIQUE 

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

Distinct120267
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size939.7 KiB
Minimum2021-07-12 08:00:07.814000
Maximum2021-08-12 14:40:49.093000
2024-11-22T14:01:51.412841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-22T14:01:51.540934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cellid
Text

The cell id

Distinct537
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
2024-11-22T14:01:51.661618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters7697088
Distinct characters16
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

Unique237 ?
Unique (%)0.2%

Sample

1st row7a74fa2b2ee08f03ddfc33ff4c263de24317c360e54a23b0ace7f48850adcd37
2nd row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
3rd row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
4th row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
5th row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
ValueCountFrequency (%)
577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377 73331
61.0%
7a74fa2b2ee08f03ddfc33ff4c263de24317c360e54a23b0ace7f48850adcd37 3703
 
3.1%
44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220 2926
 
2.4%
38b53120a0456a429398133c66bf5e68387150a56ee92b099b063b90db139a0f 2712
 
2.3%
87ded86569f4df408eabe3597c9198e0cfdb4986407407a548d3c28a34fe0d75 2351
 
2.0%
10ff0b09b508386de4f147d85b619f89a6d066056ce37f852ba77a44d6cf7403 2150
 
1.8%
ef32fbdc555331e1fe455594c04d7878910dcbac1885093a27e0129c35cb5fa3 1882
 
1.6%
28bd7b11bf894ac3c562c019620093edee6e42f2d7259e410d4593753895798c 1835
 
1.5%
ad5cdedfaf97575d809c68917a81ddeed8870832f8e3952891b52ce562b52a86 1424
 
1.2%
548e6957eeaa81398101249e1e60bdad84b38d2acec90229f387933b65731b91 1416
 
1.2%
Other values (527) 26537
 
22.1%
2024-11-22T14:01:51.876683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 764276
 
9.9%
7 761905
 
9.9%
8 710765
 
9.2%
d 619699
 
8.1%
4 565163
 
7.3%
0 494142
 
6.4%
2 480762
 
6.2%
a 465136
 
6.0%
5 420365
 
5.5%
b 401675
 
5.2%
Other values (6) 2013200
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7697088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 764276
 
9.9%
7 761905
 
9.9%
8 710765
 
9.2%
d 619699
 
8.1%
4 565163
 
7.3%
0 494142
 
6.4%
2 480762
 
6.2%
a 465136
 
6.0%
5 420365
 
5.5%
b 401675
 
5.2%
Other values (6) 2013200
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7697088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 764276
 
9.9%
7 761905
 
9.9%
8 710765
 
9.2%
d 619699
 
8.1%
4 565163
 
7.3%
0 494142
 
6.4%
2 480762
 
6.2%
a 465136
 
6.0%
5 420365
 
5.5%
b 401675
 
5.2%
Other values (6) 2013200
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7697088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 764276
 
9.9%
7 761905
 
9.9%
8 710765
 
9.2%
d 619699
 
8.1%
4 565163
 
7.3%
0 494142
 
6.4%
2 480762
 
6.2%
a 465136
 
6.0%
5 420365
 
5.5%
b 401675
 
5.2%
Other values (6) 2013200
26.2%

dbm
Real number (ℝ)

(DeciBel-Milliwatts)The received signal strength.

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19391480.04
Minimum-137
Maximum2147483647
Zeros320
Zeros (%)0.3%
Negative118861
Negative (%)98.8%
Memory size939.7 KiB
2024-11-22T14:01:52.086815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-137
5-th percentile-123
Q1-114
median-106
Q3-98
95-th percentile-24
Maximum2147483647
Range2147483784
Interquartile range (IQR)16

Descriptive statistics

Standard deviation203143831.2
Coefficient of variation (CV)10.47593225
Kurtosis105.7566527
Mean19391480.04
Median Absolute Deviation (MAD)8
Skewness10.38050548
Sum2.33215513 × 1012
Variance4.126741616 × 1016
MonotonicityNot monotonic
2024-11-22T14:01:52.206447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-107 6663
 
5.5%
-105 5921
 
4.9%
-24 5820
 
4.8%
-113 5514
 
4.6%
-103 4562
 
3.8%
-109 4271
 
3.6%
-111 4103
 
3.4%
-101 3896
 
3.2%
-99 3361
 
2.8%
-106 3131
 
2.6%
Other values (80) 73025
60.7%
ValueCountFrequency (%)
-137 1
 
< 0.1%
-135 7
 
< 0.1%
-134 19
 
< 0.1%
-133 29
 
< 0.1%
-132 94
0.1%
ValueCountFrequency (%)
2147483647 1086
 
0.9%
0 320
 
0.3%
-24 5820
4.8%
-50 1
 
< 0.1%
-51 2856
2.4%

type
Text

The technology type of the network (lte, wcdma, gsm, etc…)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-11-22T14:01:52.278296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.176756716
Min length3

Characters and Unicode

Total characters382059
Distinct characters10
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 rowlte
2nd rowlte
3rd rowlte
4th rowlte
5th rowlte
ValueCountFrequency (%)
lte 89746
74.6%
gsm 19892
 
16.5%
wcdma 10629
 
8.8%
2024-11-22T14:01:52.462561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 89746
23.5%
t 89746
23.5%
e 89746
23.5%
m 30521
 
8.0%
g 19892
 
5.2%
s 19892
 
5.2%
w 10629
 
2.8%
c 10629
 
2.8%
d 10629
 
2.8%
a 10629
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 382059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 89746
23.5%
t 89746
23.5%
e 89746
23.5%
m 30521
 
8.0%
g 19892
 
5.2%
s 19892
 
5.2%
w 10629
 
2.8%
c 10629
 
2.8%
d 10629
 
2.8%
a 10629
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 382059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 89746
23.5%
t 89746
23.5%
e 89746
23.5%
m 30521
 
8.0%
g 19892
 
5.2%
s 19892
 
5.2%
w 10629
 
2.8%
c 10629
 
2.8%
d 10629
 
2.8%
a 10629
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 382059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 89746
23.5%
t 89746
23.5%
e 89746
23.5%
m 30521
 
8.0%
g 19892
 
5.2%
s 19892
 
5.2%
w 10629
 
2.8%
c 10629
 
2.8%
d 10629
 
2.8%
a 10629
 
2.8%

Correlations

2024-11-22T14:01:52.537283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
dbmuserid
dbm1.0000.273
userid0.2731.000