Overview

Dataset statistics

Number of variables4
Number of observations100771
Missing cells0
Missing cells (%)0.0%
Total size in memory4.0 MiB
Average record size in memory42.0 B

Variable types

Text1
Numeric2
DateTime1

Dataset

Description[Steps] The step counter sensor is used to get the total number of steps taken by the user since the last reboot (power on) 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)
valueThe number of steps

Alerts

experimentid has constant value "wenetIndia"Constant
userid has 7063 (7.0%) zerosZeros

Reproduction

Analysis started2024-11-22 12:41:59.258151
Analysis finished2024-11-22 12:41:59.740087
Duration0.48 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 size1.7 MiB
2024-11-22T13:41:59.838346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1007710
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 100771
100.0%
2024-11-22T13:42:00.070573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 201542
20.0%
n 201542
20.0%
w 100771
10.0%
t 100771
10.0%
I 100771
10.0%
d 100771
10.0%
i 100771
10.0%
a 100771
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1007710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 201542
20.0%
n 201542
20.0%
w 100771
10.0%
t 100771
10.0%
I 100771
10.0%
d 100771
10.0%
i 100771
10.0%
a 100771
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1007710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 201542
20.0%
n 201542
20.0%
w 100771
10.0%
t 100771
10.0%
I 100771
10.0%
d 100771
10.0%
i 100771
10.0%
a 100771
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1007710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 201542
20.0%
n 201542
20.0%
w 100771
10.0%
t 100771
10.0%
I 100771
10.0%
d 100771
10.0%
i 100771
10.0%
a 100771
10.0%

userid
Real number (ℝ)

ZEROS 

User id

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.88025325
Minimum0
Maximum62
Zeros7063
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size787.4 KiB
2024-11-22T13:42:00.170649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median24
Q324
95-th percentile57
Maximum62
Range62
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.12194683
Coefficient of variation (CV)0.7242223861
Kurtosis0.535535902
Mean20.88025325
Median Absolute Deviation (MAD)12
Skewness0.9231199679
Sum2104124
Variance228.6732759
MonotonicityIncreasing
2024-11-22T13:42:00.264170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
24 27765
27.6%
12 22433
22.3%
35 11085
 
11.0%
9 10353
 
10.3%
4 7671
 
7.6%
0 7063
 
7.0%
43 6224
 
6.2%
62 4555
 
4.5%
18 1109
 
1.1%
17 741
 
0.7%
Other values (8) 1772
 
1.8%
ValueCountFrequency (%)
0 7063
 
7.0%
4 7671
 
7.6%
8 285
 
0.3%
9 10353
10.3%
12 22433
22.3%
ValueCountFrequency (%)
62 4555
4.5%
57 680
 
0.7%
46 120
 
0.1%
44 493
 
0.5%
43 6224
6.2%

timestamp
Date

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

Distinct100768
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size787.4 KiB
Minimum2021-07-12 08:25:21.586000
Maximum2021-08-12 14:33:39.816000
2024-11-22T13:42:00.372645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-22T13:42:00.495473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

value
Real number (ℝ)

The number of steps

Distinct62123
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49248.76863
Minimum0
Maximum163163
Zeros62
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size787.4 KiB
2024-11-22T13:42:00.608921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile520
Q14026.5
median23097
Q3124017
95-th percentile154789
Maximum163163
Range163163
Interquartile range (IQR)119990.5

Descriptive statistics

Standard deviation58111.73917
Coefficient of variation (CV)1.179963292
Kurtosis-0.9273972012
Mean49248.76863
Median Absolute Deviation (MAD)20205
Skewness0.9260876193
Sum4962847664
Variance3376974230
MonotonicityNot monotonic
2024-11-22T13:42:00.725903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
 
0.1%
99 20
 
< 0.1%
150 19
 
< 0.1%
134 18
 
< 0.1%
44 17
 
< 0.1%
45 16
 
< 0.1%
97 16
 
< 0.1%
94 16
 
< 0.1%
625 16
 
< 0.1%
78 16
 
< 0.1%
Other values (62113) 100555
99.8%
ValueCountFrequency (%)
0 62
0.1%
1 6
 
< 0.1%
2 1
 
< 0.1%
5 2
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
163163 1
< 0.1%
163162 1
< 0.1%
163160 1
< 0.1%
163158 1
< 0.1%
163151 1
< 0.1%

Correlations

2024-11-22T13:42:00.801824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
useridvalue
userid1.0000.293
value0.2931.000