Overview

Dataset statistics

Number of variables13
Number of observations87555
Missing cells376397
Missing cells (%)33.1%
Total size in memory10.0 MiB
Average record size in memory119.8 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
StillThe value of the "still" activity
OnFootThe value of the "on_foot" activity
WalkingThe value of the "walking" activity
RunningThe value of the "running" activity
InVehicleThe value of the "in_vehicle" activity
OnBicycleThe value of the "on_bicycle" activity
TiltingThe value of the "tilting" activity
UnknownThe value of the "unknown" activity

Alerts

experimentid has constant value "wenetIndia"Constant
Tilting has constant value "100.0"Constant
InVehicle is highly overall correlated with StillHigh correlation
OnBicycle is highly overall correlated with Unknown and 1 other fieldsHigh correlation
OnFoot is highly overall correlated with Running and 2 other fieldsHigh correlation
Running is highly overall correlated with OnFoot and 3 other fieldsHigh correlation
Still is highly overall correlated with InVehicle and 4 other fieldsHigh correlation
Unknown is highly overall correlated with OnBicycle and 3 other fieldsHigh correlation
Walking is highly overall correlated with OnFoot and 2 other fieldsHigh correlation
accuracy is highly overall correlated with OnBicycle and 3 other fieldsHigh correlation
Still has 13110 (15.0%) missing valuesMissing
OnFoot has 47306 (54.0%) missing valuesMissing
Walking has 47365 (54.1%) missing valuesMissing
Running has 66669 (76.1%) missing valuesMissing
InVehicle has 44296 (50.6%) missing valuesMissing
OnBicycle has 55577 (63.5%) missing valuesMissing
Tilting has 76910 (87.8%) missing valuesMissing
Unknown has 25164 (28.7%) missing valuesMissing
userid has 13816 (15.8%) zerosZeros

Reproduction

Analysis started2024-11-22 12:42:03.956370
Analysis finished2024-11-22 12:42:05.229395
Duration1.27 second
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

experimentid
Text

CONSTANT 

Experiment Id

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-11-22T13:42:05.322789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters875550
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 87555
100.0%
2024-11-22T13:42:05.563760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 175110
20.0%
n 175110
20.0%
w 87555
10.0%
t 87555
10.0%
I 87555
10.0%
d 87555
10.0%
i 87555
10.0%
a 87555
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 875550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 175110
20.0%
n 175110
20.0%
w 87555
10.0%
t 87555
10.0%
I 87555
10.0%
d 87555
10.0%
i 87555
10.0%
a 87555
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 875550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 175110
20.0%
n 175110
20.0%
w 87555
10.0%
t 87555
10.0%
I 87555
10.0%
d 87555
10.0%
i 87555
10.0%
a 87555
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 875550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 175110
20.0%
n 175110
20.0%
w 87555
10.0%
t 87555
10.0%
I 87555
10.0%
d 87555
10.0%
i 87555
10.0%
a 87555
10.0%

userid
Real number (ℝ)

ZEROS 

User id

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.0603849
Minimum0
Maximum62
Zeros13816
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:05.668460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median12
Q324
95-th percentile57
Maximum62
Range62
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.49683533
Coefficient of variation (CV)0.9669673588
Kurtosis0.5746200007
Mean17.0603849
Median Absolute Deviation (MAD)5
Skewness1.278475024
Sum1493722
Variance272.1455758
MonotonicityIncreasing
2024-11-22T13:42:05.760230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
9 22254
25.4%
12 15927
18.2%
0 13816
15.8%
24 6944
 
7.9%
57 3744
 
4.3%
17 3366
 
3.8%
8 3158
 
3.6%
4 2975
 
3.4%
49 2802
 
3.2%
43 2779
 
3.2%
Other values (9) 9790
11.2%
ValueCountFrequency (%)
0 13816
15.8%
4 2975
 
3.4%
8 3158
 
3.6%
9 22254
25.4%
12 15927
18.2%
ValueCountFrequency (%)
62 1479
 
1.7%
57 3744
4.3%
49 2802
3.2%
46 748
 
0.9%
44 2334
2.7%

timestamp
Date

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

Distinct87554
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size684.2 KiB
Minimum2021-07-12 08:00:09.823000
Maximum2021-08-12 14:40:29.584000
2024-11-22T13:42:05.872116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-22T13:42:05.994564image/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

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.22896465
Minimum23
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:06.113266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile40
Q154
median97
Q3100
95-th percentile100
Maximum100
Range77
Interquartile range (IQR)46

Descriptive statistics

Standard deviation25.06379665
Coefficient of variation (CV)0.3085573817
Kurtosis-1.038801234
Mean81.22896465
Median Absolute Deviation (MAD)3
Skewness-0.8887994774
Sum7112002
Variance628.1939026
MonotonicityNot monotonic
2024-11-22T13:42:06.236097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 24012
27.4%
40 16709
19.1%
99 14494
16.6%
96 5627
 
6.4%
97 4896
 
5.6%
98 3796
 
4.3%
94 546
 
0.6%
92 542
 
0.6%
93 523
 
0.6%
95 518
 
0.6%
Other values (68) 15892
18.2%
ValueCountFrequency (%)
23 2
 
< 0.1%
24 3
 
< 0.1%
25 6
 
< 0.1%
26 21
< 0.1%
27 33
< 0.1%
ValueCountFrequency (%)
100 24012
27.4%
99 14494
16.6%
98 3796
 
4.3%
97 4896
 
5.6%
96 5627
 
6.4%

label
Text

The activity name with highest accuracy

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-11-22T13:42:06.317461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.822637199
Min length5

Characters and Unicode

Total characters509801
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 52912
60.4%
unknown 16934
 
19.3%
tilting 10645
 
12.2%
onfoot 3796
 
4.3%
invehicle 3004
 
3.4%
onbicycle 264
 
0.3%
2024-11-22T13:42:06.511631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 119737
23.5%
i 77470
15.2%
n 68511
13.4%
t 67353
13.2%
S 52912
10.4%
o 24526
 
4.8%
U 16934
 
3.3%
k 16934
 
3.3%
w 16934
 
3.3%
T 10645
 
2.1%
Other values (10) 37845
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 509801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 119737
23.5%
i 77470
15.2%
n 68511
13.4%
t 67353
13.2%
S 52912
10.4%
o 24526
 
4.8%
U 16934
 
3.3%
k 16934
 
3.3%
w 16934
 
3.3%
T 10645
 
2.1%
Other values (10) 37845
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 509801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 119737
23.5%
i 77470
15.2%
n 68511
13.4%
t 67353
13.2%
S 52912
10.4%
o 24526
 
4.8%
U 16934
 
3.3%
k 16934
 
3.3%
w 16934
 
3.3%
T 10645
 
2.1%
Other values (10) 37845
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 509801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 119737
23.5%
i 77470
15.2%
n 68511
13.4%
t 67353
13.2%
S 52912
10.4%
o 24526
 
4.8%
U 16934
 
3.3%
k 16934
 
3.3%
w 16934
 
3.3%
T 10645
 
2.1%
Other values (10) 37845
 
7.4%

Still
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "still" activity

Distinct100
Distinct (%)0.1%
Missing13110
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean65.89200081
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:06.644762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median96
Q399
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)89

Descriptive statistics

Standard deviation39.81099591
Coefficient of variation (CV)0.6041855676
Kurtosis-1.472043175
Mean65.89200081
Median Absolute Deviation (MAD)4
Skewness-0.5813641036
Sum4905330
Variance1584.915395
MonotonicityNot monotonic
2024-11-22T13:42:06.764118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16575
18.9%
99 14486
16.5%
100 13358
15.3%
96 3871
 
4.4%
97 3850
 
4.4%
98 3599
 
4.1%
1 1892
 
2.2%
2 773
 
0.9%
3 532
 
0.6%
4 415
 
0.5%
Other values (90) 15094
17.2%
(Missing) 13110
15.0%
ValueCountFrequency (%)
1 1892
2.2%
2 773
0.9%
3 532
 
0.6%
4 415
 
0.5%
5 301
 
0.3%
ValueCountFrequency (%)
100 13358
15.3%
99 14486
16.5%
98 3599
 
4.1%
97 3850
 
4.4%
96 3871
 
4.4%

OnFoot
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "on_foot" activity

Distinct81
Distinct (%)0.2%
Missing47306
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean17.33374742
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:06.987411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q312
95-th percentile92
Maximum100
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation24.87824045
Coefficient of variation (CV)1.435248815
Kurtosis4.622698568
Mean17.33374742
Median Absolute Deviation (MAD)3
Skewness2.441898905
Sum697666
Variance618.9268478
MonotonicityNot monotonic
2024-11-22T13:42:07.111202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 17006
 
19.4%
1 6137
 
7.0%
2 1561
 
1.8%
96 941
 
1.1%
3 905
 
1.0%
4 786
 
0.9%
5 737
 
0.8%
6 688
 
0.8%
7 603
 
0.7%
8 593
 
0.7%
Other values (71) 10292
 
11.8%
(Missing) 47306
54.0%
ValueCountFrequency (%)
1 6137
7.0%
2 1561
 
1.8%
3 905
 
1.0%
4 786
 
0.9%
5 737
 
0.8%
ValueCountFrequency (%)
100 5
 
< 0.1%
99 5
 
< 0.1%
98 78
 
0.1%
97 456
0.5%
96 941
1.1%

Walking
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "walking" activity

Distinct75
Distinct (%)0.2%
Missing47365
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean17.33294352
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:07.233360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q312
95-th percentile92
Maximum100
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation24.86829218
Coefficient of variation (CV)1.434741431
Kurtosis4.633201992
Mean17.33294352
Median Absolute Deviation (MAD)3
Skewness2.444001864
Sum696611
Variance618.4319561
MonotonicityNot monotonic
2024-11-22T13:42:07.355691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 17009
 
19.4%
1 6132
 
7.0%
2 1527
 
1.7%
96 941
 
1.1%
3 902
 
1.0%
4 780
 
0.9%
5 737
 
0.8%
6 686
 
0.8%
7 606
 
0.7%
8 591
 
0.7%
Other values (65) 10279
 
11.7%
(Missing) 47365
54.1%
ValueCountFrequency (%)
1 6132
7.0%
2 1527
 
1.7%
3 902
 
1.0%
4 780
 
0.9%
5 737
 
0.8%
ValueCountFrequency (%)
100 4
 
< 0.1%
99 5
 
< 0.1%
98 78
 
0.1%
97 456
0.5%
96 941
1.1%

Running
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "running" activity

Distinct34
Distinct (%)0.2%
Missing66669
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean8.418510007
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:07.465737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median10
Q310
95-th percentile10
Maximum92
Range91
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.678514991
Coefficient of variation (CV)0.4369555882
Kurtosis57.86955297
Mean8.418510007
Median Absolute Deviation (MAD)0
Skewness2.082550302
Sum175829
Variance13.53147254
MonotonicityNot monotonic
2024-11-22T13:42:07.573209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10 16510
 
18.9%
1 2332
 
2.7%
2 833
 
1.0%
3 542
 
0.6%
4 224
 
0.3%
5 138
 
0.2%
6 88
 
0.1%
7 66
 
0.1%
8 47
 
0.1%
9 28
 
< 0.1%
Other values (24) 78
 
0.1%
(Missing) 66669
76.1%
ValueCountFrequency (%)
1 2332
2.7%
2 833
 
1.0%
3 542
 
0.6%
4 224
 
0.3%
5 138
 
0.2%
ValueCountFrequency (%)
92 1
< 0.1%
88 1
< 0.1%
79 2
< 0.1%
78 1
< 0.1%
70 1
< 0.1%

InVehicle
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "in_vehicle" activity

Distinct83
Distinct (%)0.2%
Missing44296
Missing (%)50.6%
Infinite0
Infinite (%)0.0%
Mean14.77304145
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:07.687524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q310
95-th percentile90
Maximum100
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation22.24669068
Coefficient of variation (CV)1.505897804
Kurtosis7.588253565
Mean14.77304145
Median Absolute Deviation (MAD)5
Skewness2.9232611
Sum639067
Variance494.9152462
MonotonicityNot monotonic
2024-11-22T13:42:07.808081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16996
 
19.4%
1 7881
 
9.0%
2 2544
 
2.9%
3 1159
 
1.3%
4 861
 
1.0%
96 813
 
0.9%
5 797
 
0.9%
8 735
 
0.8%
6 700
 
0.8%
7 597
 
0.7%
Other values (73) 10176
 
11.6%
(Missing) 44296
50.6%
ValueCountFrequency (%)
1 7881
9.0%
2 2544
 
2.9%
3 1159
 
1.3%
4 861
 
1.0%
5 797
 
0.9%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 2
 
< 0.1%
98 115
 
0.1%
97 587
0.7%
96 813
0.9%

OnBicycle
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "on_bicycle" activity

Distinct83
Distinct (%)0.3%
Missing55577
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean8.051379073
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:07.927806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q310
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.026975084
Coefficient of variation (CV)0.9969689678
Kurtosis55.51298017
Mean8.051379073
Median Absolute Deviation (MAD)0
Skewness6.265686753
Sum257467
Variance64.43232899
MonotonicityNot monotonic
2024-11-22T13:42:08.048806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16764
 
19.1%
1 4314
 
4.9%
2 2929
 
3.3%
3 1879
 
2.1%
4 1296
 
1.5%
5 885
 
1.0%
6 722
 
0.8%
7 493
 
0.6%
8 418
 
0.5%
9 331
 
0.4%
Other values (73) 1947
 
2.2%
(Missing) 55577
63.5%
ValueCountFrequency (%)
1 4314
4.9%
2 2929
3.3%
3 1879
2.1%
4 1296
 
1.5%
5 885
 
1.0%
ValueCountFrequency (%)
99 1
 
< 0.1%
98 4
< 0.1%
97 3
< 0.1%
96 2
< 0.1%
95 4
< 0.1%

Tilting
Real number (ℝ)

CONSTANT  MISSING 

The value of the "tilting" activity

Distinct1
Distinct (%)< 0.1%
Missing76910
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean100
Minimum100
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:08.142999image/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
Sum1064500
Variance0
MonotonicityIncreasing
2024-11-22T13:42:08.220095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
100 10645
 
12.2%
(Missing) 76910
87.8%
ValueCountFrequency (%)
100 10645
12.2%
ValueCountFrequency (%)
100 10645
12.2%

Unknown
Real number (ℝ)

HIGH CORRELATION  MISSING 

The value of the "unknown" activity

Distinct77
Distinct (%)0.1%
Missing25164
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean12.21217804
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size684.2 KiB
2024-11-22T13:42:08.324268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q340
95-th percentile40
Maximum100
Range99
Interquartile range (IQR)39

Descriptive statistics

Standard deviation17.52695595
Coefficient of variation (CV)1.435203114
Kurtosis-0.5793488218
Mean12.21217804
Median Absolute Deviation (MAD)1
Skewness1.081649472
Sum761930
Variance307.1941848
MonotonicityNot monotonic
2024-11-22T13:42:08.448098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 26358
30.1%
40 16499
18.8%
2 15512
17.7%
3 3154
 
3.6%
4 86
 
0.1%
8 80
 
0.1%
15 46
 
0.1%
31 41
 
< 0.1%
23 38
 
< 0.1%
46 33
 
< 0.1%
Other values (67) 544
 
0.6%
(Missing) 25164
28.7%
ValueCountFrequency (%)
1 26358
30.1%
2 15512
17.7%
3 3154
 
3.6%
4 86
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
100 2
< 0.1%
93 1
 
< 0.1%
92 4
< 0.1%
91 1
 
< 0.1%
90 2
< 0.1%

Correlations

2024-11-22T13:42:08.526862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
InVehicleOnBicycleOnFootRunningStillUnknownWalkingaccuracyuserid
InVehicle1.000-0.0080.1330.378-0.5140.2410.136-0.4740.013
OnBicycle-0.0081.000-0.1040.477-0.4310.662-0.104-0.688-0.044
OnFoot0.133-0.1041.000-0.832-0.5570.1720.999-0.3360.079
Running0.3780.477-0.8321.0000.0330.927-0.859-0.658-0.151
Still-0.514-0.431-0.5570.0331.000-0.735-0.5580.9180.110
Unknown0.2410.6620.1720.927-0.7351.0000.175-0.8130.042
Walking0.136-0.1040.999-0.859-0.5580.1751.000-0.3360.081
accuracy-0.474-0.688-0.336-0.6580.918-0.813-0.3361.0000.087
userid0.013-0.0440.079-0.1510.1100.0420.0810.0871.000