Overview

Dataset statistics

Number of variables13
Number of observations1994317
Missing cells9692378
Missing cells (%)37.4%
Total size in memory243.0 MiB
Average record size in memory127.8 B

Variable types

Text2
Numeric10
DateTime1

Dataset

Description[unitless] Sensor that returns a label identifying the activity performed by the user, accurately detected using low power signals from multiple sensors in the device. This is achieved using Google’s Activity Recognition APIs. Possible activities are: still, in_vehicle, on_bycicle, on_foot, running, tilting, walking. 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)
accuracyThe highest accuracy for possible activities
labelThe activity name with highest accuracy
InVehicleThe value of the "in_vehicle" activity
OnBicycleThe value of the "on_bicycle" activity
OnFootThe value of the "on_foot" activity
RunningThe value of the "running" activity
StillThe value of the "still" activity
UnknownThe value of the "unknown" activity
WalkingThe value of the "walking" activity
TiltingThe value of the "tilting" activity

Alerts

experimentid has constant value "wenetItaly"Constant
Tilting has constant value "100.0"Constant
OnBicycle is highly overall correlated with Running and 2 other fieldsHigh correlation
OnFoot is highly overall correlated with Running and 1 other fieldsHigh correlation
Running is highly overall correlated with OnBicycle and 4 other fieldsHigh correlation
Still is highly overall correlated with Unknown and 1 other fieldsHigh correlation
Unknown is highly overall correlated with OnBicycle and 3 other fieldsHigh correlation
Walking is highly overall correlated with OnFoot and 1 other fieldsHigh correlation
accuracy is highly overall correlated with OnBicycle and 3 other fieldsHigh correlation
InVehicle has 1215042 (60.9%) missing valuesMissing
OnBicycle has 1388466 (69.6%) missing valuesMissing
OnFoot has 1264710 (63.4%) missing valuesMissing
Running has 1555835 (78.0%) missing valuesMissing
Still has 310560 (15.6%) missing valuesMissing
Unknown has 910796 (45.7%) missing valuesMissing
Walking has 1267616 (63.6%) missing valuesMissing
Tilting has 1779353 (89.2%) missing valuesMissing

Reproduction

Analysis started2024-11-23 08:33:23.097067
Analysis finished2024-11-23 08:33:38.430993
Duration15.33 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 size49.5 MiB
2024-11-23T09:33:38.498786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters19943170
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 1994317
100.0%
2024-11-23T09:33:38.704675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3988634
20.0%
t 3988634
20.0%
w 1994317
10.0%
n 1994317
10.0%
I 1994317
10.0%
a 1994317
10.0%
l 1994317
10.0%
y 1994317
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19943170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3988634
20.0%
t 3988634
20.0%
w 1994317
10.0%
n 1994317
10.0%
I 1994317
10.0%
a 1994317
10.0%
l 1994317
10.0%
y 1994317
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19943170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3988634
20.0%
t 3988634
20.0%
w 1994317
10.0%
n 1994317
10.0%
I 1994317
10.0%
a 1994317
10.0%
l 1994317
10.0%
y 1994317
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19943170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3988634
20.0%
t 3988634
20.0%
w 1994317
10.0%
n 1994317
10.0%
I 1994317
10.0%
a 1994317
10.0%
l 1994317
10.0%
y 1994317
10.0%

userid
Real number (ℝ)

User id

Distinct198
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.3382095
Minimum0
Maximum264
Zeros10046
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:38.828688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q163
median127
Q3198
95-th percentile253
Maximum264
Range264
Interquartile range (IQR)135

Descriptive statistics

Standard deviation78.90372792
Coefficient of variation (CV)0.6100573698
Kurtosis-1.269469616
Mean129.3382095
Median Absolute Deviation (MAD)69
Skewness0.009343314869
Sum257941390
Variance6225.79828
MonotonicityIncreasing
2024-11-23T09:33:38.949367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 43715
 
2.2%
175 37887
 
1.9%
190 37088
 
1.9%
78 35029
 
1.8%
163 34705
 
1.7%
8 30855
 
1.5%
191 30732
 
1.5%
207 29171
 
1.5%
188 28639
 
1.4%
50 27412
 
1.4%
Other values (188) 1659084
83.2%
ValueCountFrequency (%)
0 10046
 
0.5%
1 25761
1.3%
2 8433
 
0.4%
3 8413
 
0.4%
4 5317
 
0.3%
ValueCountFrequency (%)
264 10169
0.5%
263 15284
0.8%
262 10065
0.5%
260 825
 
< 0.1%
259 1561
 
0.1%

timestamp
Date

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

Distinct1993201
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size30.4 MiB
Minimum2020-11-16 07:00:00.253000
Maximum2020-12-11 21:59:58.580000
2024-11-23T09:33:39.068192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-23T09:33:39.190893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

accuracy
Real number (ℝ)

HIGH CORRELATION 

The highest accuracy for possible activities

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.98089221
Minimum21
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:39.308969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile40
Q166
median99
Q3100
95-th percentile100
Maximum100
Range79
Interquartile range (IQR)34

Descriptive statistics

Standard deviation24.52304139
Coefficient of variation (CV)0.292007393
Kurtosis-0.6142174146
Mean83.98089221
Median Absolute Deviation (MAD)1
Skewness-1.114820579
Sum167484521
Variance601.3795591
MonotonicityNot monotonic
2024-11-23T09:33:39.438174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 886394
44.4%
40 326851
 
16.4%
99 222363
 
11.1%
96 88248
 
4.4%
97 74309
 
3.7%
98 63635
 
3.2%
41 17645
 
0.9%
92 13033
 
0.7%
44 10475
 
0.5%
94 10262
 
0.5%
Other values (70) 281102
 
14.1%
ValueCountFrequency (%)
21 15
 
< 0.1%
22 24
 
< 0.1%
23 46
 
< 0.1%
24 77
 
< 0.1%
25 204
< 0.1%
ValueCountFrequency (%)
100 886394
44.4%
99 222363
 
11.1%
98 63635
 
3.2%
97 74309
 
3.7%
96 88248
 
4.4%

label
Text

The activity name with highest accuracy

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.4 MiB
2024-11-23T09:33:39.525361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.769961847
Min length5

Characters and Unicode

Total characters11507133
Distinct characters20
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 rowStill
2nd rowStill
3rd rowStill
4th rowStill
5th rowStill
ValueCountFrequency (%)
still 1240400
62.2%
unknown 354615
 
17.8%
tilting 214964
 
10.8%
onfoot 113654
 
5.7%
invehicle 64743
 
3.2%
onbicycle 5941
 
0.3%
2024-11-23T09:33:39.733276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2766448
24.0%
i 1741012
15.1%
t 1569018
13.6%
n 1463147
12.7%
S 1240400
10.8%
o 581923
 
5.1%
U 354615
 
3.1%
k 354615
 
3.1%
w 354615
 
3.1%
T 214964
 
1.9%
Other values (10) 866376
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11507133
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2766448
24.0%
i 1741012
15.1%
t 1569018
13.6%
n 1463147
12.7%
S 1240400
10.8%
o 581923
 
5.1%
U 354615
 
3.1%
k 354615
 
3.1%
w 354615
 
3.1%
T 214964
 
1.9%
Other values (10) 866376
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11507133
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2766448
24.0%
i 1741012
15.1%
t 1569018
13.6%
n 1463147
12.7%
S 1240400
10.8%
o 581923
 
5.1%
U 354615
 
3.1%
k 354615
 
3.1%
w 354615
 
3.1%
T 214964
 
1.9%
Other values (10) 866376
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11507133
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2766448
24.0%
i 1741012
15.1%
t 1569018
13.6%
n 1463147
12.7%
S 1240400
10.8%
o 581923
 
5.1%
U 354615
 
3.1%
k 354615
 
3.1%
w 354615
 
3.1%
T 214964
 
1.9%
Other values (10) 866376
 
7.5%

InVehicle
Real number (ℝ)

MISSING 

The value of the "in_vehicle" activity

Distinct100
Distinct (%)< 0.1%
Missing1215042
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean16.66211158
Minimum0
Maximum100
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:39.860810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q312
95-th percentile93
Maximum100
Range100
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.67659678
Coefficient of variation (CV)1.420984169
Kurtosis5.880770078
Mean16.66211158
Median Absolute Deviation (MAD)3
Skewness2.653370969
Sum12984367
Variance560.5812349
MonotonicityNot monotonic
2024-11-23T09:33:39.985697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 345138
 
17.3%
1 114672
 
5.7%
2 36235
 
1.8%
96 17974
 
0.9%
3 13872
 
0.7%
8 13421
 
0.7%
23 12664
 
0.6%
4 11591
 
0.6%
97 11278
 
0.6%
6 9863
 
0.5%
Other values (90) 192567
 
9.7%
(Missing) 1215042
60.9%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 114672
5.7%
2 36235
 
1.8%
3 13872
 
0.7%
4 11591
 
0.6%
ValueCountFrequency (%)
100 1623
 
0.1%
99 217
 
< 0.1%
98 2243
 
0.1%
97 11278
0.6%
96 17974
0.9%

OnBicycle
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "on_bicycle" activity

Distinct96
Distinct (%)< 0.1%
Missing1388466
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean8.265321011
Minimum0
Maximum100
Zeros634
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:40.108881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median10
Q310
95-th percentile10
Maximum100
Range100
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.933214095
Coefficient of variation (CV)1.080806672
Kurtosis63.50653304
Mean8.265321011
Median Absolute Deviation (MAD)0
Skewness7.047584339
Sum5007553
Variance79.80231406
MonotonicityNot monotonic
2024-11-23T09:33:40.236724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 340188
 
17.1%
1 72065
 
3.6%
2 56592
 
2.8%
3 32864
 
1.6%
4 24270
 
1.2%
5 16545
 
0.8%
6 12916
 
0.6%
8 8609
 
0.4%
7 8002
 
0.4%
9 4503
 
0.2%
Other values (86) 29297
 
1.5%
(Missing) 1388466
69.6%
ValueCountFrequency (%)
0 634
 
< 0.1%
1 72065
3.6%
2 56592
2.8%
3 32864
1.6%
4 24270
 
1.2%
ValueCountFrequency (%)
100 817
< 0.1%
99 476
< 0.1%
98 372
< 0.1%
97 579
< 0.1%
96 366
< 0.1%

OnFoot
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "on_foot" activity

Distinct101
Distinct (%)< 0.1%
Missing1264710
Missing (%)63.4%
Infinite0
Infinite (%)0.0%
Mean22.88047812
Minimum0
Maximum100
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:40.364399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median10
Q317
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)7

Descriptive statistics

Standard deviation30.25872667
Coefficient of variation (CV)1.322469159
Kurtosis1.441128059
Mean22.88047812
Median Absolute Deviation (MAD)2
Skewness1.77839606
Sum16693757
Variance915.5905396
MonotonicityNot monotonic
2024-11-23T09:33:40.497147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 343765
 
17.2%
1 66163
 
3.3%
2 23728
 
1.2%
96 21743
 
1.1%
97 15037
 
0.8%
11 12775
 
0.6%
8 11340
 
0.6%
3 11034
 
0.6%
4 10812
 
0.5%
5 10116
 
0.5%
Other values (91) 203094
 
10.2%
(Missing) 1264710
63.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 66163
3.3%
2 23728
 
1.2%
3 11034
 
0.6%
4 10812
 
0.5%
ValueCountFrequency (%)
100 2463
 
0.1%
99 1071
 
0.1%
98 9455
0.5%
97 15037
0.8%
96 21743
1.1%

Running
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "running" activity

Distinct101
Distinct (%)< 0.1%
Missing1555835
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean8.795188856
Minimum0
Maximum100
Zeros2646
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:40.620224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median10
Q310
95-th percentile10
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.516701624
Coefficient of variation (CV)0.8546378874
Kurtosis85.9062301
Mean8.795188856
Median Absolute Deviation (MAD)0
Skewness8.006275269
Sum3856532
Variance56.5008033
MonotonicityNot monotonic
2024-11-23T09:33:40.746993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 336274
 
16.9%
1 54729
 
2.7%
2 19403
 
1.0%
3 8043
 
0.4%
4 4579
 
0.2%
5 2969
 
0.1%
0 2646
 
0.1%
6 1756
 
0.1%
8 1654
 
0.1%
7 1146
 
0.1%
Other values (91) 5283
 
0.3%
(Missing) 1555835
78.0%
ValueCountFrequency (%)
0 2646
 
0.1%
1 54729
2.7%
2 19403
 
1.0%
3 8043
 
0.4%
4 4579
 
0.2%
ValueCountFrequency (%)
100 13
 
< 0.1%
99 374
< 0.1%
98 309
< 0.1%
97 307
< 0.1%
96 265
< 0.1%

Still
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "still" activity

Distinct101
Distinct (%)< 0.1%
Missing310560
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean70.35320774
Minimum0
Maximum100
Zeros570
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:40.877889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median99
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)90

Descriptive statistics

Standard deviation39.77557451
Coefficient of variation (CV)0.5653697363
Kurtosis-1.24566243
Mean70.35320774
Median Absolute Deviation (MAD)1
Skewness-0.7766170567
Sum118457706
Variance1582.096327
MonotonicityNot monotonic
2024-11-23T09:33:41.009616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 666407
33.4%
10 337477
16.9%
99 220599
 
11.1%
98 51513
 
2.6%
96 48089
 
2.4%
97 47415
 
2.4%
1 43237
 
2.2%
2 18568
 
0.9%
44 10164
 
0.5%
3 9623
 
0.5%
Other values (91) 230665
 
11.6%
(Missing) 310560
15.6%
ValueCountFrequency (%)
0 570
 
< 0.1%
1 43237
2.2%
2 18568
0.9%
3 9623
 
0.5%
4 7329
 
0.4%
ValueCountFrequency (%)
100 666407
33.4%
99 220599
 
11.1%
98 51513
 
2.6%
97 47415
 
2.4%
96 48089
 
2.4%

Unknown
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "unknown" activity

Distinct93
Distinct (%)< 0.1%
Missing910796
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean14.76966021
Minimum0
Maximum100
Zeros3221
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:41.141274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q340
95-th percentile40
Maximum100
Range100
Interquartile range (IQR)39

Descriptive statistics

Standard deviation18.90244282
Coefficient of variation (CV)1.279815686
Kurtosis-0.8266214415
Mean14.76966021
Median Absolute Deviation (MAD)1
Skewness0.8455174574
Sum16003237
Variance357.3023444
MonotonicityNot monotonic
2024-11-23T09:33:41.277781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 407560
20.4%
40 322799
 
16.2%
2 244932
 
12.3%
3 56959
 
2.9%
41 13580
 
0.7%
8 3259
 
0.2%
0 3221
 
0.2%
50 2430
 
0.1%
31 1787
 
0.1%
15 1752
 
0.1%
Other values (83) 25242
 
1.3%
(Missing) 910796
45.7%
ValueCountFrequency (%)
0 3221
 
0.2%
1 407560
20.4%
2 244932
12.3%
3 56959
 
2.9%
4 1594
 
0.1%
ValueCountFrequency (%)
100 120
< 0.1%
98 52
< 0.1%
96 76
< 0.1%
94 109
< 0.1%
93 18
 
< 0.1%

Walking
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "walking" activity

Distinct101
Distinct (%)< 0.1%
Missing1267616
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean22.61792677
Minimum0
Maximum100
Zeros156
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:41.399893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median10
Q317
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)7

Descriptive statistics

Standard deviation29.99912418
Coefficient of variation (CV)1.326342794
Kurtosis1.566551426
Mean22.61792677
Median Absolute Deviation (MAD)1
Skewness1.810989977
Sum16436470
Variance899.9474518
MonotonicityNot monotonic
2024-11-23T09:33:41.526438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 343793
 
17.2%
1 66579
 
3.3%
2 22803
 
1.1%
96 21476
 
1.1%
97 14729
 
0.7%
11 12829
 
0.6%
3 11155
 
0.6%
8 11037
 
0.6%
4 10445
 
0.5%
5 10128
 
0.5%
Other values (91) 201727
 
10.1%
(Missing) 1267616
63.6%
ValueCountFrequency (%)
0 156
 
< 0.1%
1 66579
3.3%
2 22803
 
1.1%
3 11155
 
0.6%
4 10445
 
0.5%
ValueCountFrequency (%)
100 2277
 
0.1%
99 696
 
< 0.1%
98 9141
0.5%
97 14729
0.7%
96 21476
1.1%

Tilting
Real number (ℝ)

CONSTANT  MISSING 

The value of the "tilting" activity

Distinct1
Distinct (%)< 0.1%
Missing1779353
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean100
Minimum100
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 MiB
2024-11-23T09:33:41.623890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean100
Median Absolute Deviation (MAD)0
Skewness0
Sum21496400
Variance0
MonotonicityIncreasing
2024-11-23T09:33:41.710817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
100 214964
 
10.8%
(Missing) 1779353
89.2%
ValueCountFrequency (%)
100 214964
10.8%
ValueCountFrequency (%)
100 214964
10.8%

Correlations

2024-11-23T09:33:41.776561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
InVehicleOnBicycleOnFootRunningStillUnknownWalkingaccuracyuserid
InVehicle1.000-0.109-0.0370.321-0.3480.113-0.032-0.3240.017
OnBicycle-0.1091.000-0.2940.632-0.3840.707-0.297-0.664-0.001
OnFoot-0.037-0.2941.000-0.777-0.458-0.0720.9890.0070.001
Running0.3210.632-0.7771.0000.0800.822-0.865-0.574-0.012
Still-0.348-0.384-0.4580.0801.000-0.707-0.4600.947-0.022
Unknown0.1130.707-0.0720.822-0.7071.000-0.063-0.795-0.030
Walking-0.032-0.2970.989-0.865-0.460-0.0631.000-0.0020.002
accuracy-0.324-0.6640.007-0.5740.947-0.795-0.0021.000-0.013
userid0.017-0.0010.001-0.012-0.022-0.0300.002-0.0131.000