Overview

Dataset statistics

Number of variables6
Number of observations2751450
Missing cells0
Missing cells (%)0.0%
Total size in memory350.4 MiB
Average record size in memory133.5 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 "wenetItaly"Constant

Reproduction

Analysis started2024-11-23 11:27:22.871859
Analysis finished2024-11-23 11:27:36.120415
Duration13.25 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 size68.2 MiB
2024-11-23T12:27:36.174942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters27514500
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 2751450
100.0%
2024-11-23T12:27:36.428684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5502900
20.0%
t 5502900
20.0%
w 2751450
10.0%
n 2751450
10.0%
I 2751450
10.0%
a 2751450
10.0%
l 2751450
10.0%
y 2751450
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27514500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5502900
20.0%
t 5502900
20.0%
w 2751450
10.0%
n 2751450
10.0%
I 2751450
10.0%
a 2751450
10.0%
l 2751450
10.0%
y 2751450
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27514500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5502900
20.0%
t 5502900
20.0%
w 2751450
10.0%
n 2751450
10.0%
I 2751450
10.0%
a 2751450
10.0%
l 2751450
10.0%
y 2751450
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27514500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5502900
20.0%
t 5502900
20.0%
w 2751450
10.0%
n 2751450
10.0%
I 2751450
10.0%
a 2751450
10.0%
l 2751450
10.0%
y 2751450
10.0%

userid
Real number (ℝ)

User id

Distinct204
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.5622752
Minimum0
Maximum264
Zeros1300
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size42.0 MiB
2024-11-23T12:27:36.550598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q166
median136
Q3209
95-th percentile254
Maximum264
Range264
Interquartile range (IQR)143

Descriptive statistics

Standard deviation80.82012591
Coefficient of variation (CV)0.59181883
Kurtosis-1.289821372
Mean136.5622752
Median Absolute Deviation (MAD)71
Skewness-0.1305529297
Sum375744272
Variance6531.892752
MonotonicityIncreasing
2024-11-23T12:27:36.671403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131 48750
 
1.8%
80 48221
 
1.8%
66 45516
 
1.7%
202 45156
 
1.6%
221 43778
 
1.6%
114 42782
 
1.6%
84 41872
 
1.5%
212 41775
 
1.5%
162 40237
 
1.5%
91 40234
 
1.5%
Other values (194) 2313129
84.1%
ValueCountFrequency (%)
0 1300
 
< 0.1%
1 37356
1.4%
2 18465
0.7%
3 784
 
< 0.1%
4 21931
0.8%
ValueCountFrequency (%)
264 183
 
< 0.1%
263 23747
0.9%
262 21649
0.8%
260 59
 
< 0.1%
259 11086
0.4%

timestamp
Date

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

Distinct2749697
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size42.0 MiB
Minimum2020-11-16 07:00:00.493000
Maximum2020-12-11 21:59:59.643000
2024-11-23T12:27:36.790421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-23T12:27:36.911059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cellid
Text

The cell id

Distinct7206
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size209.9 MiB
2024-11-23T12:27:37.033630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters176092800
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

Unique2818 ?
Unique (%)0.1%

Sample

1st row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
2nd row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
3rd row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
4th row577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377
5th rowddb2e6fd2ed9f5464db50ee1c3401dd236ed2175a62f81c7d91fe5dcce3e0cea
ValueCountFrequency (%)
577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377 1349115
49.0%
44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220 460300
 
16.7%
38b53120a0456a429398133c66bf5e68387150a56ee92b099b063b90db139a0f 69531
 
2.5%
4e4e723bf6ff48f2863e540d4b0b54739112501afe62783a5228c70c71126760 18060
 
0.7%
f2b6f3b91c38ff49fefefa909cd72606f4750188aea8765ab442382e99d9deb0 18041
 
0.7%
dca122f10b7b6e27cfb35c168a9d1dc0a685ab7fbfa441e82c8e2a87c4298613 16534
 
0.6%
765db146bc4518c2e7ec25bebfafba2100e52773b0fab7ce15ba9534026ea1b7 14599
 
0.5%
af704e6fc913791cba133fa8af2108f0f9b43d457c87b36f17079f810194bb56 14302
 
0.5%
43c49a1f86e68ffca1601f54cf294cf6efbeed6f1872b69cd02877640137d3ac 13414
 
0.5%
c716d0ffbd4087be42e08d7088f91b8d768d19ced11ef0e47ccff88e15090eb3 10190
 
0.4%
Other values (7196) 767364
27.9%
2024-11-23T12:27:37.253283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 16556537
 
9.4%
7 15898336
 
9.0%
8 14716912
 
8.4%
4 14624522
 
8.3%
d 12351487
 
7.0%
2 12037657
 
6.8%
0 11218108
 
6.4%
a 10003329
 
5.7%
b 9885044
 
5.6%
5 9816126
 
5.6%
Other values (6) 48984742
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176092800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 16556537
 
9.4%
7 15898336
 
9.0%
8 14716912
 
8.4%
4 14624522
 
8.3%
d 12351487
 
7.0%
2 12037657
 
6.8%
0 11218108
 
6.4%
a 10003329
 
5.7%
b 9885044
 
5.6%
5 9816126
 
5.6%
Other values (6) 48984742
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176092800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 16556537
 
9.4%
7 15898336
 
9.0%
8 14716912
 
8.4%
4 14624522
 
8.3%
d 12351487
 
7.0%
2 12037657
 
6.8%
0 11218108
 
6.4%
a 10003329
 
5.7%
b 9885044
 
5.6%
5 9816126
 
5.6%
Other values (6) 48984742
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176092800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 16556537
 
9.4%
7 15898336
 
9.0%
8 14716912
 
8.4%
4 14624522
 
8.3%
d 12351487
 
7.0%
2 12037657
 
6.8%
0 11218108
 
6.4%
a 10003329
 
5.7%
b 9885044
 
5.6%
5 9816126
 
5.6%
Other values (6) 48984742
27.8%

dbm
Real number (ℝ)

(DeciBel-Milliwatts)The received signal strength.

Distinct145
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15349825.21
Minimum-141
Maximum2147483647
Zeros2384
Zeros (%)0.1%
Negative2729322
Negative (%)99.2%
Memory size42.0 MiB
2024-11-23T12:27:37.376592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-141
5-th percentile-129
Q1-116
median-109
Q3-97
95-th percentile-51
Maximum2147483647
Range2147483788
Interquartile range (IQR)19

Descriptive statistics

Standard deviation180909135.4
Coefficient of variation (CV)11.78574564
Kurtosis134.9093127
Mean15349825.21
Median Absolute Deviation (MAD)10
Skewness11.70082111
Sum4.223427656 × 1013
Variance3.272811529 × 1016
MonotonicityNot monotonic
2024-11-23T12:27:37.505008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-113 236346
 
8.6%
-51 173131
 
6.3%
-111 96980
 
3.5%
-109 86541
 
3.1%
-107 85153
 
3.1%
-120 81436
 
3.0%
-105 74822
 
2.7%
-103 68945
 
2.5%
-112 68679
 
2.5%
-110 64782
 
2.4%
Other values (135) 1714635
62.3%
ValueCountFrequency (%)
-141 116
 
< 0.1%
-140 9320
0.3%
-139 326
 
< 0.1%
-138 662
 
< 0.1%
-137 1606
 
0.1%
ValueCountFrequency (%)
2147483647 19667
0.7%
28671 3
 
< 0.1%
101 1
 
< 0.1%
99 1
 
< 0.1%
45 1
 
< 0.1%

type
Text

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

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.3 MiB
2024-11-23T12:27:37.583285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.549850442
Min length3

Characters and Unicode

Total characters9767236
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 rowwcdma
2nd rowwcdma
3rd rowlte
4th rowwcdma
5th rowlte
ValueCountFrequency (%)
lte 1846913
67.1%
wcdma 756443
27.5%
gsm 148094
 
5.4%
2024-11-23T12:27:37.771729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 1846913
18.9%
t 1846913
18.9%
e 1846913
18.9%
m 904537
9.3%
w 756443
7.7%
c 756443
7.7%
d 756443
7.7%
a 756443
7.7%
g 148094
 
1.5%
s 148094
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9767236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1846913
18.9%
t 1846913
18.9%
e 1846913
18.9%
m 904537
9.3%
w 756443
7.7%
c 756443
7.7%
d 756443
7.7%
a 756443
7.7%
g 148094
 
1.5%
s 148094
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9767236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1846913
18.9%
t 1846913
18.9%
e 1846913
18.9%
m 904537
9.3%
w 756443
7.7%
c 756443
7.7%
d 756443
7.7%
a 756443
7.7%
g 148094
 
1.5%
s 148094
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9767236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1846913
18.9%
t 1846913
18.9%
e 1846913
18.9%
m 904537
9.3%
w 756443
7.7%
c 756443
7.7%
d 756443
7.7%
a 756443
7.7%
g 148094
 
1.5%
s 148094
 
1.5%

Correlations

2024-11-23T12:27:37.848908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
dbmuserid
dbm1.000-0.045
userid-0.0451.000