Overview

Dataset statistics

Number of variables24
Number of observations168095
Missing cells2544778
Missing cells (%)63.1%
Total size in memory44.2 MiB
Average record size in memory275.4 B

Variable types

Numeric11
Text13

Dataset

Description[unitless] Question and answers. 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.
CreatorXiaoyue Li
AuthorXiaoyue Li
URL
Copyright(c) Knowledge Diversity, Trento University 2024

Alerts

population has constant value "0.0"Constant
code is highly overall correlated with gidHigh correlation
gid is highly overall correlated with codeHigh correlation
questiontimestamp2 has 63681 (37.9%) missing valuesMissing
notificationtimestamp2 has 63681 (37.9%) missing valuesMissing
answertimestamp2 has 63681 (37.9%) missing valuesMissing
answerduration has 63681 (37.9%) missing valuesMissing
delta has 63681 (37.9%) missing valuesMissing
what has 63681 (37.9%) missing valuesMissing
how has 159448 (94.9%) missing valuesMissing
where has 88402 (52.6%) missing valuesMissing
withwhom has 79840 (47.5%) missing valuesMissing
mood has 79840 (47.5%) missing valuesMissing
latitude has 141268 (84.0%) missing valuesMissing
longitude has 141268 (84.0%) missing valuesMissing
altitude has 141268 (84.0%) missing valuesMissing
gid has 166140 (98.8%) missing valuesMissing
osm_id has 166140 (98.8%) missing valuesMissing
code has 166140 (98.8%) missing valuesMissing
fclass has 166140 (98.8%) missing valuesMissing
name has 166140 (98.8%) missing valuesMissing
population has 168071 (> 99.9%) missing valuesMissing
type has 166447 (99.0%) missing valuesMissing
coordinates has 166140 (98.8%) missing valuesMissing
answerduration has 16159 (9.6%) zerosZeros
delta has 2758 (1.6%) zerosZeros

Reproduction

Analysis started2024-09-24 08:54:38.733520
Analysis finished2024-09-24 08:54:41.698315
Duration2.96 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

userid
Real number (ℝ)

Distinct158
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.50320949
Minimum0
Maximum157
Zeros1065
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:41.922369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q139
median79
Q3118
95-th percentile150
Maximum157
Range157
Interquartile range (IQR)79

Descriptive statistics

Standard deviation45.61129998
Coefficient of variation (CV)0.5810119138
Kurtosis-1.200181691
Mean78.50320949
Median Absolute Deviation (MAD)40
Skewness-8.872441011 × 10-5
Sum13195997
Variance2080.390686
MonotonicityIncreasing
2024-09-24T10:54:42.057998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 1067
 
0.6%
131 1066
 
0.6%
58 1066
 
0.6%
156 1066
 
0.6%
144 1066
 
0.6%
140 1065
 
0.6%
0 1065
 
0.6%
22 1065
 
0.6%
90 1064
 
0.6%
89 1064
 
0.6%
Other values (148) 157441
93.7%
ValueCountFrequency (%)
0 1065
0.6%
1 1064
0.6%
2 1064
0.6%
3 1063
0.6%
4 1064
0.6%
ValueCountFrequency (%)
157 1063
0.6%
156 1066
0.6%
155 1063
0.6%
154 1064
0.6%
153 1064
0.6%

dt
Text

Distinct1063
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-09-24T10:54:42.201816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters3193805
Distinct characters13
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 row1900-05-09 03:30:00
2nd row1900-05-09 04:00:00
3rd row1900-05-09 04:30:00
4th row1900-05-09 05:00:00
5th row1900-05-09 05:30:00
ValueCountFrequency (%)
1900-05-11 7588
 
2.3%
1900-05-10 7587
 
2.3%
1900-05-17 7587
 
2.3%
1900-05-23 7585
 
2.3%
1900-05-12 7584
 
2.3%
1900-05-13 7584
 
2.3%
1900-05-14 7584
 
2.3%
1900-05-15 7584
 
2.3%
1900-05-16 7584
 
2.3%
1900-05-18 7584
 
2.3%
Other values (67) 260339
77.4%
2024-09-24T10:54:42.454689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1256459
39.3%
1 358161
 
11.2%
- 336190
 
10.5%
: 336190
 
10.5%
9 200015
 
6.3%
5 175173
 
5.5%
168095
 
5.3%
2 122906
 
3.8%
3 106342
 
3.3%
6 51352
 
1.6%
Other values (3) 82922
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3193805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1256459
39.3%
1 358161
 
11.2%
- 336190
 
10.5%
: 336190
 
10.5%
9 200015
 
6.3%
5 175173
 
5.5%
168095
 
5.3%
2 122906
 
3.8%
3 106342
 
3.3%
6 51352
 
1.6%
Other values (3) 82922
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3193805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1256459
39.3%
1 358161
 
11.2%
- 336190
 
10.5%
: 336190
 
10.5%
9 200015
 
6.3%
5 175173
 
5.5%
168095
 
5.3%
2 122906
 
3.8%
3 106342
 
3.3%
6 51352
 
1.6%
Other values (3) 82922
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3193805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1256459
39.3%
1 358161
 
11.2%
- 336190
 
10.5%
: 336190
 
10.5%
9 200015
 
6.3%
5 175173
 
5.5%
168095
 
5.3%
2 122906
 
3.8%
3 106342
 
3.3%
6 51352
 
1.6%
Other values (3) 82922
 
2.6%

questiontimestamp2
Text

MISSING 

Distinct905
Distinct (%)0.9%
Missing63681
Missing (%)37.9%
Memory size3.2 MiB
2024-09-24T10:54:42.616517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1983866
Distinct characters13
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 row1900-05-09 06:32:00
2nd row1900-05-09 07:02:00
3rd row1900-05-09 07:32:00
4th row1900-05-09 08:02:00
5th row1900-05-09 08:32:00
ValueCountFrequency (%)
1900-05-14 5910
 
2.8%
1900-05-22 5866
 
2.8%
1900-05-16 5853
 
2.8%
1900-05-17 5819
 
2.8%
1900-05-11 5802
 
2.8%
1900-05-15 5797
 
2.8%
1900-05-18 5705
 
2.7%
1900-05-12 5662
 
2.7%
1900-05-13 5656
 
2.7%
1900-05-10 5627
 
2.7%
Other values (81) 151131
72.4%
2024-09-24T10:54:42.869334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 637475
32.1%
1 256260
12.9%
- 208828
 
10.5%
: 208828
 
10.5%
2 163290
 
8.2%
5 130997
 
6.6%
9 127178
 
6.4%
104414
 
5.3%
3 72708
 
3.7%
6 22421
 
1.1%
Other values (3) 51467
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 637475
32.1%
1 256260
12.9%
- 208828
 
10.5%
: 208828
 
10.5%
2 163290
 
8.2%
5 130997
 
6.6%
9 127178
 
6.4%
104414
 
5.3%
3 72708
 
3.7%
6 22421
 
1.1%
Other values (3) 51467
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 637475
32.1%
1 256260
12.9%
- 208828
 
10.5%
: 208828
 
10.5%
2 163290
 
8.2%
5 130997
 
6.6%
9 127178
 
6.4%
104414
 
5.3%
3 72708
 
3.7%
6 22421
 
1.1%
Other values (3) 51467
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 637475
32.1%
1 256260
12.9%
- 208828
 
10.5%
: 208828
 
10.5%
2 163290
 
8.2%
5 130997
 
6.6%
9 127178
 
6.4%
104414
 
5.3%
3 72708
 
3.7%
6 22421
 
1.1%
Other values (3) 51467
 
2.6%
Distinct22346
Distinct (%)21.4%
Missing63681
Missing (%)37.9%
Memory size3.2 MiB
2024-09-24T10:54:43.065340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1983866
Distinct characters13
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

Unique15301 ?
Unique (%)14.7%

Sample

1st row1900-05-09 08:49:43
2nd row1900-05-09 08:49:43
3rd row1900-05-09 08:49:43
4th row1900-05-09 08:49:44
5th row1900-05-09 08:49:44
ValueCountFrequency (%)
1900-05-14 5915
 
2.8%
1900-05-22 5866
 
2.8%
1900-05-16 5849
 
2.8%
1900-05-17 5820
 
2.8%
1900-05-11 5802
 
2.8%
1900-05-15 5797
 
2.8%
1900-05-18 5700
 
2.7%
1900-05-13 5666
 
2.7%
1900-05-12 5653
 
2.7%
1900-05-10 5622
 
2.7%
Other values (11300) 151138
72.4%
2024-09-24T10:54:43.349805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 455124
22.9%
1 309859
15.6%
2 212062
10.7%
- 208828
10.5%
: 208828
10.5%
9 161429
 
8.1%
5 138335
 
7.0%
104414
 
5.3%
3 82321
 
4.1%
4 28760
 
1.4%
Other values (3) 73906
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 455124
22.9%
1 309859
15.6%
2 212062
10.7%
- 208828
10.5%
: 208828
10.5%
9 161429
 
8.1%
5 138335
 
7.0%
104414
 
5.3%
3 82321
 
4.1%
4 28760
 
1.4%
Other values (3) 73906
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 455124
22.9%
1 309859
15.6%
2 212062
10.7%
- 208828
10.5%
: 208828
10.5%
9 161429
 
8.1%
5 138335
 
7.0%
104414
 
5.3%
3 82321
 
4.1%
4 28760
 
1.4%
Other values (3) 73906
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 455124
22.9%
1 309859
15.6%
2 212062
10.7%
- 208828
10.5%
: 208828
10.5%
9 161429
 
8.1%
5 138335
 
7.0%
104414
 
5.3%
3 82321
 
4.1%
4 28760
 
1.4%
Other values (3) 73906
 
3.7%

answertimestamp2
Text

MISSING 

Distinct89336
Distinct (%)85.6%
Missing63681
Missing (%)37.9%
Memory size3.2 MiB
2024-09-24T10:54:43.590795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1983866
Distinct characters13
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

Unique84161 ?
Unique (%)80.6%

Sample

1st row1900-05-09 09:04:09
2nd row1900-05-09 08:49:43
3rd row1900-05-09 08:49:43
4th row1900-05-09 08:49:44
5th row1900-05-09 08:49:44
ValueCountFrequency (%)
1900-05-14 5882
 
2.8%
1900-05-22 5882
 
2.8%
1900-05-16 5853
 
2.8%
1900-05-17 5803
 
2.8%
1900-05-15 5789
 
2.8%
1900-05-13 5764
 
2.8%
1900-05-11 5763
 
2.8%
1900-05-18 5621
 
2.7%
1900-05-12 5615
 
2.7%
1900-05-10 5611
 
2.7%
Other values (49251) 151245
72.4%
2024-09-24T10:54:43.913005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 448043
22.6%
1 295117
14.9%
- 208828
10.5%
: 208828
10.5%
5 161413
 
8.1%
9 148906
 
7.5%
2 142523
 
7.2%
104414
 
5.3%
3 88947
 
4.5%
4 65815
 
3.3%
Other values (3) 111032
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 448043
22.6%
1 295117
14.9%
- 208828
10.5%
: 208828
10.5%
5 161413
 
8.1%
9 148906
 
7.5%
2 142523
 
7.2%
104414
 
5.3%
3 88947
 
4.5%
4 65815
 
3.3%
Other values (3) 111032
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 448043
22.6%
1 295117
14.9%
- 208828
10.5%
: 208828
10.5%
5 161413
 
8.1%
9 148906
 
7.5%
2 142523
 
7.2%
104414
 
5.3%
3 88947
 
4.5%
4 65815
 
3.3%
Other values (3) 111032
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1983866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 448043
22.6%
1 295117
14.9%
- 208828
10.5%
: 208828
10.5%
5 161413
 
8.1%
9 148906
 
7.5%
2 142523
 
7.2%
104414
 
5.3%
3 88947
 
4.5%
4 65815
 
3.3%
Other values (3) 111032
 
5.6%

answerduration
Real number (ℝ)

MISSING  ZEROS 

Distinct22337
Distinct (%)21.4%
Missing63681
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean10113.03299
Minimum0
Maximum462960
Zeros16159
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:44.059288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15760.25
median8106
Q311800.75
95-th percentile25856.4
Maximum462960
Range462960
Interquartile range (IQR)6040.5

Descriptive statistics

Standard deviation11450.0818
Coefficient of variation (CV)1.132210466
Kurtosis162.6405062
Mean10113.03299
Median Absolute Deviation (MAD)2844
Skewness8.165215475
Sum1055942227
Variance131104373.2
MonotonicityNot monotonic
2024-09-24T10:54:44.179926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16159
 
9.6%
7670 23
 
< 0.1%
7330 23
 
< 0.1%
7905 23
 
< 0.1%
7480 23
 
< 0.1%
7569 22
 
< 0.1%
6333 22
 
< 0.1%
6903 22
 
< 0.1%
7167 21
 
< 0.1%
7303 21
 
< 0.1%
Other values (22327) 88055
52.4%
(Missing) 63681
37.9%
ValueCountFrequency (%)
0 16159
9.6%
1658 1
 
< 0.1%
1744 1
 
< 0.1%
1751 1
 
< 0.1%
1858 1
 
< 0.1%
ValueCountFrequency (%)
462960 1
< 0.1%
454911 1
< 0.1%
429378 1
< 0.1%
359610 1
< 0.1%
343956 1
< 0.1%

delta
Real number (ℝ)

MISSING  ZEROS 

Distinct87057
Distinct (%)83.4%
Missing63681
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean2904941.433
Minimum0
Maximum73881685
Zeros2758
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:44.299863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1101560.75
median808813
Q33226259
95-th percentile14123335.2
Maximum73881685
Range73881685
Interquartile range (IQR)3124698.25

Descriptive statistics

Standard deviation5048764.894
Coefficient of variation (CV)1.737991973
Kurtosis13.05614022
Mean2904941.433
Median Absolute Deviation (MAD)808806
Skewness3.100031657
Sum3.033165548 × 1011
Variance2.549002695 × 1013
MonotonicityNot monotonic
2024-09-24T10:54:44.430260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2758
 
1.6%
3 2523
 
1.5%
4 1957
 
1.2%
5 1649
 
1.0%
2 1612
 
1.0%
6 1094
 
0.7%
7 822
 
0.5%
8 637
 
0.4%
1 579
 
0.3%
9 489
 
0.3%
Other values (87047) 90294
53.7%
(Missing) 63681
37.9%
ValueCountFrequency (%)
0 2758
1.6%
1 579
 
0.3%
2 1612
1.0%
3 2523
1.5%
4 1957
1.2%
ValueCountFrequency (%)
73881685 1
< 0.1%
66692075 1
< 0.1%
66224031 1
< 0.1%
65404555 1
< 0.1%
59502403 1
< 0.1%

what
Text

MISSING 

Distinct22
Distinct (%)< 0.1%
Missing63681
Missing (%)37.9%
Memory size2.5 MiB
2024-09-24T10:54:44.553323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length40
Median length35
Mean length11.70900454
Min length4

Characters and Unicode

Total characters1222584
Distinct characters46
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 rowSleeping
2nd rowI will go to sleep
3rd rowI will go to sleep
4th rowI will go to sleep
5th rowI will go to sleep
ValueCountFrequency (%)
studying 17459
 
8.2%
to 14383
 
6.8%
i 13466
 
6.3%
will 13466
 
6.3%
go 13466
 
6.3%
sleep 13466
 
6.3%
sleeping 11618
 
5.5%
eating 9156
 
4.3%
etc 8976
 
4.2%
en 8600
 
4.0%
Other values (35) 88895
41.7%
2024-09-24T10:54:44.799911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 127125
 
10.4%
108537
 
8.9%
i 88058
 
7.2%
t 84365
 
6.9%
o 81407
 
6.7%
l 74459
 
6.1%
n 67963
 
5.6%
g 64975
 
5.3%
s 47288
 
3.9%
a 43018
 
3.5%
Other values (36) 435389
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1222584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 127125
 
10.4%
108537
 
8.9%
i 88058
 
7.2%
t 84365
 
6.9%
o 81407
 
6.7%
l 74459
 
6.1%
n 67963
 
5.6%
g 64975
 
5.3%
s 47288
 
3.9%
a 43018
 
3.5%
Other values (36) 435389
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1222584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 127125
 
10.4%
108537
 
8.9%
i 88058
 
7.2%
t 84365
 
6.9%
o 81407
 
6.7%
l 74459
 
6.1%
n 67963
 
5.6%
g 64975
 
5.3%
s 47288
 
3.9%
a 43018
 
3.5%
Other values (36) 435389
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1222584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 127125
 
10.4%
108537
 
8.9%
i 88058
 
7.2%
t 84365
 
6.9%
o 81407
 
6.7%
l 74459
 
6.1%
n 67963
 
5.6%
g 64975
 
5.3%
s 47288
 
3.9%
a 43018
 
3.5%
Other values (36) 435389
35.6%

how
Text

MISSING 

Distinct6
Distinct (%)0.1%
Missing159448
Missing (%)94.9%
Memory size1.4 MiB
2024-09-24T10:54:44.892267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length8
Mean length6.702324506
Min length6

Characters and Unicode

Total characters57955
Distinct characters17
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 rowBy bike
2nd rowBy car
3rd rowBy car
4th rowBy car
5th rowBy car
ValueCountFrequency (%)
by 8647
50.0%
foot 3169
 
18.3%
car 2479
 
14.3%
bus 1551
 
9.0%
train 1241
 
7.2%
bike 164
 
0.9%
motorbike 43
 
0.2%
2024-09-24T10:54:45.083925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 8647
14.9%
y 8647
14.9%
8647
14.9%
o 6424
11.1%
t 4453
7.7%
r 3763
6.5%
a 3720
6.4%
f 3169
 
5.5%
c 2479
 
4.3%
b 1758
 
3.0%
Other values (7) 6248
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 8647
14.9%
y 8647
14.9%
8647
14.9%
o 6424
11.1%
t 4453
7.7%
r 3763
6.5%
a 3720
6.4%
f 3169
 
5.5%
c 2479
 
4.3%
b 1758
 
3.0%
Other values (7) 6248
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 8647
14.9%
y 8647
14.9%
8647
14.9%
o 6424
11.1%
t 4453
7.7%
r 3763
6.5%
a 3720
6.4%
f 3169
 
5.5%
c 2479
 
4.3%
b 1758
 
3.0%
Other values (7) 6248
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 8647
14.9%
y 8647
14.9%
8647
14.9%
o 6424
11.1%
t 4453
7.7%
r 3763
6.5%
a 3720
6.4%
f 3169
 
5.5%
c 2479
 
4.3%
b 1758
 
3.0%
Other values (7) 6248
10.8%

where
Text

MISSING 

Distinct16
Distinct (%)< 0.1%
Missing88402
Missing (%)52.6%
Memory size2.1 MiB
2024-09-24T10:54:45.185570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length30
Median length26
Mean length11.07075904
Min length3

Characters and Unicode

Total characters882262
Distinct characters41
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 rowRelatives Home
2nd rowRelatives Home
3rd rowRelatives Home
4th rowRelatives Home
5th rowRelatives Home
ValueCountFrequency (%)
home 49252
34.2%
relatives 14174
 
9.8%
10070
 
7.0%
classroom 9737
 
6.8%
place 5244
 
3.6%
laboratory 5185
 
3.6%
study 4552
 
3.2%
hall 4552
 
3.2%
outdoors 4531
 
3.1%
house 4388
 
3.0%
Other values (19) 32357
22.5%
2024-09-24T10:54:45.407716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 109763
12.4%
o 99430
11.3%
64349
 
7.3%
m 60602
 
6.9%
s 55879
 
6.3%
a 55464
 
6.3%
r 55189
 
6.3%
H 53640
 
6.1%
t 44533
 
5.0%
l 38259
 
4.3%
Other values (31) 245154
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 109763
12.4%
o 99430
11.3%
64349
 
7.3%
m 60602
 
6.9%
s 55879
 
6.3%
a 55464
 
6.3%
r 55189
 
6.3%
H 53640
 
6.1%
t 44533
 
5.0%
l 38259
 
4.3%
Other values (31) 245154
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 109763
12.4%
o 99430
11.3%
64349
 
7.3%
m 60602
 
6.9%
s 55879
 
6.3%
a 55464
 
6.3%
r 55189
 
6.3%
H 53640
 
6.1%
t 44533
 
5.0%
l 38259
 
4.3%
Other values (31) 245154
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 109763
12.4%
o 99430
11.3%
64349
 
7.3%
m 60602
 
6.9%
s 55879
 
6.3%
a 55464
 
6.3%
r 55189
 
6.3%
H 53640
 
6.1%
t 44533
 
5.0%
l 38259
 
4.3%
Other values (31) 245154
27.8%

withwhom
Text

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing79840
Missing (%)47.5%
Memory size1.9 MiB
2024-09-24T10:54:45.501533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.563197553
Min length5

Characters and Unicode

Total characters667490
Distinct characters23
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 rowRelative(s)
2nd rowRelative(s)
3rd rowAlone
4th rowAlone
5th rowAlone
ValueCountFrequency (%)
alone 41269
46.8%
friend(s 14751
 
16.7%
relative(s 8633
 
9.8%
classmate(s 7677
 
8.7%
partner 6897
 
7.8%
roommate(s 6265
 
7.1%
colleague(s 1470
 
1.7%
other 1293
 
1.5%
2024-09-24T10:54:45.701399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 98358
14.7%
n 62917
9.4%
l 60519
9.1%
o 55269
 
8.3%
s 54150
 
8.1%
A 41269
 
6.2%
( 38796
 
5.8%
) 38796
 
5.8%
a 38619
 
5.8%
t 30765
 
4.6%
Other values (13) 148032
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 667490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 98358
14.7%
n 62917
9.4%
l 60519
9.1%
o 55269
 
8.3%
s 54150
 
8.1%
A 41269
 
6.2%
( 38796
 
5.8%
) 38796
 
5.8%
a 38619
 
5.8%
t 30765
 
4.6%
Other values (13) 148032
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 667490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 98358
14.7%
n 62917
9.4%
l 60519
9.1%
o 55269
 
8.3%
s 54150
 
8.1%
A 41269
 
6.2%
( 38796
 
5.8%
) 38796
 
5.8%
a 38619
 
5.8%
t 30765
 
4.6%
Other values (13) 148032
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 667490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 98358
14.7%
n 62917
9.4%
l 60519
9.1%
o 55269
 
8.3%
s 54150
 
8.1%
A 41269
 
6.2%
( 38796
 
5.8%
) 38796
 
5.8%
a 38619
 
5.8%
t 30765
 
4.6%
Other values (13) 148032
22.2%

mood
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing79840
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean3.8026854
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:45.807001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7571803238
Coefficient of variation (CV)0.1991172669
Kurtosis2.059408726
Mean3.8026854
Median Absolute Deviation (MAD)0
Skewness-0.9201782253
Sum335606
Variance0.5733220427
MonotonicityNot monotonic
2024-09-24T10:54:45.899272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
4 52527
31.2%
3 20382
 
12.1%
5 11714
 
7.0%
2 2150
 
1.3%
1 1482
 
0.9%
(Missing) 79840
47.5%
ValueCountFrequency (%)
1 1482
 
0.9%
2 2150
 
1.3%
3 20382
 
12.1%
4 52527
31.2%
5 11714
 
7.0%
ValueCountFrequency (%)
5 11714
 
7.0%
4 52527
31.2%
3 20382
 
12.1%
2 2150
 
1.3%
1 1482
 
0.9%

week
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2024-09-24T10:54:45.970767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1344760
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 row1st week
2nd row1st week
3rd row1st week
4th row1st week
5th row1st week
ValueCountFrequency (%)
week 168095
50.0%
1st 118007
35.1%
2nd 50088
 
14.9%
2024-09-24T10:54:46.154829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 336190
25.0%
168095
12.5%
w 168095
12.5%
k 168095
12.5%
1 118007
 
8.8%
s 118007
 
8.8%
t 118007
 
8.8%
2 50088
 
3.7%
n 50088
 
3.7%
d 50088
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1344760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 336190
25.0%
168095
12.5%
w 168095
12.5%
k 168095
12.5%
1 118007
 
8.8%
s 118007
 
8.8%
t 118007
 
8.8%
2 50088
 
3.7%
n 50088
 
3.7%
d 50088
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1344760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 336190
25.0%
168095
12.5%
w 168095
12.5%
k 168095
12.5%
1 118007
 
8.8%
s 118007
 
8.8%
t 118007
 
8.8%
2 50088
 
3.7%
n 50088
 
3.7%
d 50088
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1344760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 336190
25.0%
168095
12.5%
w 168095
12.5%
k 168095
12.5%
1 118007
 
8.8%
s 118007
 
8.8%
t 118007
 
8.8%
2 50088
 
3.7%
n 50088
 
3.7%
d 50088
 
3.7%

latitude
Real number (ℝ)

MISSING 

Distinct10367
Distinct (%)38.6%
Missing141268
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean45.98730973
Minimum37.3871
Maximum50.08577333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:46.435043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum37.3871
5-th percentile45.632227
Q146.0469
median46.06502222
Q346.0691
95-th percentile46.19170939
Maximum50.08577333
Range12.69867333
Interquartile range (IQR)0.0222

Descriptive statistics

Standard deviation0.4620216799
Coefficient of variation (CV)0.01004672121
Kurtosis102.2981417
Mean45.98730973
Median Absolute Deviation (MAD)0.00727778
Skewness-8.188076116
Sum1233701.558
Variance0.2134640327
MonotonicityNot monotonic
2024-09-24T10:54:46.566191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.8201 378
 
0.2%
46.0798 305
 
0.2%
46.067 303
 
0.2%
46.0992 303
 
0.2%
46.0694 291
 
0.2%
46.2109 260
 
0.2%
45.8641 259
 
0.2%
46.0667 250
 
0.1%
46.0611 246
 
0.1%
46.0663 238
 
0.1%
Other values (10357) 23994
 
14.3%
(Missing) 141268
84.0%
ValueCountFrequency (%)
37.3871 7
< 0.1%
39.21696364 1
 
< 0.1%
39.21698 1
 
< 0.1%
39.217 2
 
< 0.1%
39.217025 1
 
< 0.1%
ValueCountFrequency (%)
50.08577333 1
< 0.1%
50.08577 1
< 0.1%
50.08573636 1
< 0.1%
50.08572564 1
< 0.1%
50.08572069 1
< 0.1%

longitude
Real number (ℝ)

MISSING 

Distinct11948
Distinct (%)44.5%
Missing141268
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean11.18180229
Minimum-5.9888
Maximum16.41363704
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)< 0.1%
Memory size1.3 MiB
2024-09-24T10:54:46.691180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5.9888
5-th percentile10.9442
Q111.1169
median11.12556667
Q311.14251334
95-th percentile11.7208
Maximum16.41363704
Range22.40243704
Interquartile range (IQR)0.025613335

Descriptive statistics

Standard deviation0.5386111478
Coefficient of variation (CV)0.04816854508
Kurtosis301.826472
Mean11.18180229
Median Absolute Deviation (MAD)0.00946667
Skewness-4.802934546
Sum299974.2101
Variance0.2901019685
MonotonicityNot monotonic
2024-09-24T10:54:46.809311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1199 330
 
0.2%
11.1316 330
 
0.2%
12.2916 292
 
0.2%
11.134 280
 
0.2%
11.129 270
 
0.2%
11.0924 233
 
0.1%
11.1449 199
 
0.1%
11.1272 189
 
0.1%
11.1502 184
 
0.1%
11.1231 173
 
0.1%
Other values (11938) 24347
 
14.5%
(Missing) 141268
84.0%
ValueCountFrequency (%)
-5.9888 7
< 0.1%
7.747 3
< 0.1%
7.747046667 1
 
< 0.1%
7.747048148 1
 
< 0.1%
7.7471 1
 
< 0.1%
ValueCountFrequency (%)
16.41363704 1
< 0.1%
16.41344545 1
< 0.1%
16.41344231 1
< 0.1%
16.41342258 1
< 0.1%
16.4134125 1
< 0.1%

altitude
Real number (ℝ)

MISSING 

Distinct241
Distinct (%)0.9%
Missing141268
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean1.880002535
Minimum-18
Maximum1060.681854
Zeros12
Zeros (%)< 0.1%
Negative26554
Negative (%)15.8%
Memory size1.3 MiB
2024-09-24T10:54:46.928859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum1060.681854
Range1078.681854
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.85091396
Coefficient of variation (CV)17.47386684
Kurtosis306.4537198
Mean1.880002535
Median Absolute Deviation (MAD)0
Skewness15.1760688
Sum50434.82801
Variance1079.182548
MonotonicityNot monotonic
2024-09-24T10:54:47.057091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 26553
 
15.8%
0 12
 
< 0.1%
262 4
 
< 0.1%
65 3
 
< 0.1%
263 3
 
< 0.1%
249 3
 
< 0.1%
252 3
 
< 0.1%
245 2
 
< 0.1%
238 2
 
< 0.1%
259 2
 
< 0.1%
Other values (231) 240
 
0.1%
(Missing) 141268
84.0%
ValueCountFrequency (%)
-18 1
 
< 0.1%
-1 26553
15.8%
0 12
 
< 0.1%
33.6656 1
 
< 0.1%
45.33434 1
 
< 0.1%
ValueCountFrequency (%)
1060.681854 1
< 0.1%
1059.764319 1
< 0.1%
1056.616359 1
< 0.1%
1054.62897 1
< 0.1%
931 1
< 0.1%

gid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct147
Distinct (%)7.5%
Missing166140
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean2232692.912
Minimum567
Maximum4585114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:47.177641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum567
5-th percentile7874
Q1742315
median3200456
Q33373916
95-th percentile3523873
Maximum4585114
Range4584547
Interquartile range (IQR)2631601

Descriptive statistics

Standard deviation1458625.498
Coefficient of variation (CV)0.6533032333
Kurtosis-1.535198549
Mean2232692.912
Median Absolute Deviation (MAD)256598
Skewness-0.3842181767
Sum4364914642
Variance2.127588344 × 1012
MonotonicityNot monotonic
2024-09-24T10:54:47.304068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3373916 405
 
0.2%
7874 192
 
0.1%
3372274 154
 
0.1%
3389224 154
 
0.1%
2943858 114
 
0.1%
742315 92
 
0.1%
772636 86
 
0.1%
3200456 75
 
< 0.1%
743319 72
 
< 0.1%
741151 67
 
< 0.1%
Other values (137) 544
 
0.3%
(Missing) 166140
98.8%
ValueCountFrequency (%)
567 2
 
< 0.1%
977 2
 
< 0.1%
1777 1
 
< 0.1%
7874 192
0.1%
7875 2
 
< 0.1%
ValueCountFrequency (%)
4585114 1
< 0.1%
4585112 2
< 0.1%
4576144 1
< 0.1%
4553483 1
< 0.1%
4550049 2
< 0.1%

osm_id
Real number (ℝ)

MISSING 

Distinct147
Distinct (%)7.5%
Missing166140
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean467725109.5
Minimum65608561
Maximum1.081679294 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:47.427468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum65608561
5-th percentile106077349
Q1149753711
median218008250
Q3218923891
95-th percentile882460310
Maximum1.081679294 × 1010
Range1.075118438 × 1010
Interquartile range (IQR)69170180

Descriptive statistics

Standard deviation1188726408
Coefficient of variation (CV)2.541506504
Kurtosis32.41409059
Mean467725109.5
Median Absolute Deviation (MAD)28762759
Skewness5.61976248
Sum9.144025891 × 1011
Variance1.413070473 × 1018
MonotonicityNot monotonic
2024-09-24T10:54:47.567877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
218008250 405
 
0.2%
564884221 192
 
0.1%
217897668 154
 
0.1%
218923891 154
 
0.1%
189245491 114
 
0.1%
106077349 92
 
0.1%
120210327 86
 
0.1%
200221570 75
 
< 0.1%
106406070 72
 
< 0.1%
105602479 67
 
< 0.1%
Other values (137) 544
 
0.3%
(Missing) 166140
98.8%
ValueCountFrequency (%)
65608561 4
< 0.1%
70259362 1
 
< 0.1%
97548080 1
 
< 0.1%
102779829 3
< 0.1%
104720377 2
< 0.1%
ValueCountFrequency (%)
1.081679294 × 10102
 
< 0.1%
9930095450 1
 
< 0.1%
9363013553 1
 
< 0.1%
7987971963 24
< 0.1%
7079797034 3
 
< 0.1%

code
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)1.4%
Missing166140
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean1702.330946
Minimum1050
Maximum7204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:47.675277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1050
5-th percentile1500
Q11500
median1500
Q31500
95-th percentile2601
Maximum7204
Range6154
Interquartile range (IQR)0

Descriptive statistics

Standard deviation627.7870554
Coefficient of variation (CV)0.3687808512
Kurtosis23.61535817
Mean1702.330946
Median Absolute Deviation (MAD)0
Skewness4.350079643
Sum3328057
Variance394116.5869
MonotonicityNot monotonic
2024-09-24T10:54:47.777019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1500 1648
 
1.0%
2601 194
 
0.1%
1050 24
 
< 0.1%
5204 20
 
< 0.1%
2602 14
 
< 0.1%
2301 8
 
< 0.1%
2401 6
 
< 0.1%
2516 4
 
< 0.1%
2515 4
 
< 0.1%
5260 4
 
< 0.1%
Other values (17) 29
 
< 0.1%
(Missing) 166140
98.8%
ValueCountFrequency (%)
1050 24
 
< 0.1%
1500 1648
1.0%
2031 1
 
< 0.1%
2205 1
 
< 0.1%
2301 8
 
< 0.1%
ValueCountFrequency (%)
7204 1
 
< 0.1%
7203 2
< 0.1%
5601 2
< 0.1%
5332 2
< 0.1%
5303 3
< 0.1%

fclass
Text

MISSING 

Distinct27
Distinct (%)1.4%
Missing166140
Missing (%)98.8%
Memory size1.3 MiB
2024-09-24T10:54:47.869967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length15
Median length8
Mean length7.560102302
Min length3

Characters and Unicode

Total characters14780
Distinct characters22
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

Unique8 ?
Unique (%)0.4%

Sample

1st rowbuilding
2nd rowindustrial
3rd rowbuilding
4th rowbuilding
5th rowbuilding
ValueCountFrequency (%)
building 1648
84.3%
bank 194
 
9.9%
locality 24
 
1.2%
crossing 20
 
1.0%
atm 14
 
0.7%
restaurant 8
 
0.4%
hotel 6
 
0.3%
bookshop 4
 
0.2%
butcher 4
 
0.2%
parking 4
 
0.2%
Other values (17) 29
 
1.5%
2024-09-24T10:54:48.087990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 3371
22.8%
n 1890
12.8%
b 1855
12.6%
l 1713
11.6%
g 1678
11.4%
u 1664
11.3%
d 1654
11.2%
a 275
 
1.9%
k 205
 
1.4%
t 80
 
0.5%
Other values (12) 395
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3371
22.8%
n 1890
12.8%
b 1855
12.6%
l 1713
11.6%
g 1678
11.4%
u 1664
11.3%
d 1654
11.2%
a 275
 
1.9%
k 205
 
1.4%
t 80
 
0.5%
Other values (12) 395
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3371
22.8%
n 1890
12.8%
b 1855
12.6%
l 1713
11.6%
g 1678
11.4%
u 1664
11.3%
d 1654
11.2%
a 275
 
1.9%
k 205
 
1.4%
t 80
 
0.5%
Other values (12) 395
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3371
22.8%
n 1890
12.8%
b 1855
12.6%
l 1713
11.6%
g 1678
11.4%
u 1664
11.3%
d 1654
11.2%
a 275
 
1.9%
k 205
 
1.4%
t 80
 
0.5%
Other values (12) 395
 
2.7%

name
Text

MISSING 

Distinct21
Distinct (%)1.1%
Missing166140
Missing (%)98.8%
Memory size1.3 MiB
2024-09-24T10:54:48.199528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length38
Median length2
Mean length4.886956522
Min length2

Characters and Unicode

Total characters9554
Distinct characters41
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

Unique6 ?
Unique (%)0.3%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N
ValueCountFrequency (%)
n 1701
65.1%
cassa 194
 
7.4%
rurale 192
 
7.3%
alta 192
 
7.3%
valsugana 192
 
7.3%
lungolago 24
 
0.9%
e 8
 
0.3%
hotel 6
 
0.2%
antica 6
 
0.2%
rosa 6
 
0.2%
Other values (41) 92
 
3.5%
2024-09-24T10:54:48.433029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1702
17.8%
\ 1701
17.8%
a 1463
15.3%
658
 
6.9%
l 645
 
6.8%
s 604
 
6.3%
u 409
 
4.3%
e 257
 
2.7%
n 255
 
2.7%
g 245
 
2.6%
Other values (31) 1615
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1702
17.8%
\ 1701
17.8%
a 1463
15.3%
658
 
6.9%
l 645
 
6.8%
s 604
 
6.3%
u 409
 
4.3%
e 257
 
2.7%
n 255
 
2.7%
g 245
 
2.6%
Other values (31) 1615
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1702
17.8%
\ 1701
17.8%
a 1463
15.3%
658
 
6.9%
l 645
 
6.8%
s 604
 
6.3%
u 409
 
4.3%
e 257
 
2.7%
n 255
 
2.7%
g 245
 
2.6%
Other values (31) 1615
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1702
17.8%
\ 1701
17.8%
a 1463
15.3%
658
 
6.9%
l 645
 
6.8%
s 604
 
6.3%
u 409
 
4.3%
e 257
 
2.7%
n 255
 
2.7%
g 245
 
2.6%
Other values (31) 1615
16.9%

population
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)4.2%
Missing168071
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-09-24T10:54:48.539109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-09-24T10:54:48.622158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 24
 
< 0.1%
(Missing) 168071
> 99.9%
ValueCountFrequency (%)
0 24
< 0.1%
ValueCountFrequency (%)
0 24
< 0.1%

type
Text

MISSING 

Distinct10
Distinct (%)0.6%
Missing166447
Missing (%)99.0%
Memory size1.3 MiB
2024-09-24T10:54:48.686600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length2
Mean length2.737864078
Min length2

Characters and Unicode

Total characters4512
Distinct characters22
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

Unique3 ?
Unique (%)0.2%

Sample

1st row\N
2nd rowhouse
3rd row\N
4th row\N
5th row\N
ValueCountFrequency (%)
n 1414
85.8%
apartments 92
 
5.6%
house 87
 
5.3%
hut 29
 
1.8%
boathouse 18
 
1.1%
church 3
 
0.2%
farm_auxiliary 2
 
0.1%
industrial 1
 
0.1%
residential 1
 
0.1%
train_station 1
 
0.1%
2024-09-24T10:54:48.887084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 1414
31.3%
N 1414
31.3%
t 236
 
5.2%
a 212
 
4.7%
s 200
 
4.4%
e 199
 
4.4%
h 140
 
3.1%
u 140
 
3.1%
o 124
 
2.7%
r 102
 
2.3%
Other values (12) 331
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 1414
31.3%
N 1414
31.3%
t 236
 
5.2%
a 212
 
4.7%
s 200
 
4.4%
e 199
 
4.4%
h 140
 
3.1%
u 140
 
3.1%
o 124
 
2.7%
r 102
 
2.3%
Other values (12) 331
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 1414
31.3%
N 1414
31.3%
t 236
 
5.2%
a 212
 
4.7%
s 200
 
4.4%
e 199
 
4.4%
h 140
 
3.1%
u 140
 
3.1%
o 124
 
2.7%
r 102
 
2.3%
Other values (12) 331
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 1414
31.3%
N 1414
31.3%
t 236
 
5.2%
a 212
 
4.7%
s 200
 
4.4%
e 199
 
4.4%
h 140
 
3.1%
u 140
 
3.1%
o 124
 
2.7%
r 102
 
2.3%
Other values (12) 331
 
7.3%

coordinates
Text

MISSING 

Distinct147
Distinct (%)7.5%
Missing166140
Missing (%)98.8%
Memory size1.7 MiB
2024-09-24T10:54:49.047251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length802
Median length436
Mean length227.6337596
Min length25

Characters and Unicode

Total characters445024
Distinct characters17
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

Unique62 ?
Unique (%)3.2%

Sample

1st row[(11.2227828, 46.1749667), (11.2230031, 46.1750353), (11.2230666, 46.1749375), (11.2228464, 46.1748689), (11.2227828, 46.1749667)]
2nd row[(11.7937218, 45.7999569), (11.7937336, 45.7999866), (11.7937525, 45.8000341), (11.7938225, 45.8001291), (11.7939063, 45.8001942), (11.7939472, 45.8002363), (11.7939497, 45.8002341), (11.7948695, 45.7991892), (11.7948545, 45.799189), (11.7948532, 45.7992007), (11.794845, 45.7992048), (11.7948376, 45.7992096), (11.7948343, 45.7992123), (11.7944633, 45.7996461), (11.7940698, 45.7994583), (11.7937239, 45.7998697), (11.7937218, 45.7999569)]
3rd row[(11.22681, 46.0283445), (11.2268181, 46.0284832), (11.2270017, 46.028478), (11.2269935, 46.0283393), (11.22681, 46.0283445)]
4th row[(11.7158349, 45.8200596), (11.7159617, 45.8201145), (11.716012, 45.8200565), (11.7160253, 45.8200617), (11.71607, 45.8200091), (11.7160825, 45.8200134), (11.7161249, 45.8199606), (11.7161394, 45.8199653), (11.7161879, 45.819907), (11.7160658, 45.8198516), (11.7160184, 45.8199119), (11.7160016, 45.8199035), (11.7159608, 45.8199572), (11.715945, 45.8199523), (11.7158997, 45.8200055), (11.715886, 45.8200016), (11.7158349, 45.8200596)]
5th row[(11.7158349, 45.8200596), (11.7159617, 45.8201145), (11.716012, 45.8200565), (11.7160253, 45.8200617), (11.71607, 45.8200091), (11.7160825, 45.8200134), (11.7161249, 45.8199606), (11.7161394, 45.8199653), (11.7161879, 45.819907), (11.7160658, 45.8198516), (11.7160184, 45.8199119), (11.7160016, 45.8199035), (11.7159608, 45.8199572), (11.715945, 45.8199523), (11.7158997, 45.8200055), (11.715886, 45.8200016), (11.7158349, 45.8200596)]
ValueCountFrequency (%)
11.7158349 810
 
2.3%
45.8200596 810
 
2.3%
11.7159617 405
 
1.2%
45.8201145 405
 
1.2%
11.716012 405
 
1.2%
45.8200565 405
 
1.2%
11.7160253 405
 
1.2%
45.8200617 405
 
1.2%
11.71607 405
 
1.2%
45.8200091 405
 
1.2%
Other values (1604) 29804
86.0%
2024-09-24T10:54:49.371394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 67387
15.1%
6 35508
 
8.0%
5 34747
 
7.8%
. 34664
 
7.8%
, 32709
 
7.3%
32709
 
7.3%
4 31869
 
7.2%
0 29076
 
6.5%
9 28471
 
6.4%
8 22420
 
5.0%
Other values (7) 95464
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 445024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 67387
15.1%
6 35508
 
8.0%
5 34747
 
7.8%
. 34664
 
7.8%
, 32709
 
7.3%
32709
 
7.3%
4 31869
 
7.2%
0 29076
 
6.5%
9 28471
 
6.4%
8 22420
 
5.0%
Other values (7) 95464
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 445024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 67387
15.1%
6 35508
 
8.0%
5 34747
 
7.8%
. 34664
 
7.8%
, 32709
 
7.3%
32709
 
7.3%
4 31869
 
7.2%
0 29076
 
6.5%
9 28471
 
6.4%
8 22420
 
5.0%
Other values (7) 95464
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 445024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 67387
15.1%
6 35508
 
8.0%
5 34747
 
7.8%
. 34664
 
7.8%
, 32709
 
7.3%
32709
 
7.3%
4 31869
 
7.2%
0 29076
 
6.5%
9 28471
 
6.4%
8 22420
 
5.0%
Other values (7) 95464
21.5%

Correlations

2024-09-24T10:54:49.475431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
altitudeanswerdurationcodedeltagidlatitudelongitudemoodosm_iduserid
altitude1.0000.038-0.0070.0070.013-0.0160.0190.0090.0530.024
answerduration0.0381.0000.0130.367-0.1070.035-0.0060.040-0.0420.015
code-0.0070.0131.000-0.052-0.5420.047-0.1080.0710.476-0.109
delta0.0070.367-0.0521.000-0.0470.053-0.0050.052-0.110-0.020
gid0.013-0.107-0.542-0.0471.000-0.1760.480-0.0540.240-0.217
latitude-0.0160.0350.0470.053-0.1761.000-0.119-0.022-0.129-0.173
longitude0.019-0.006-0.108-0.0050.480-0.1191.0000.0520.2840.012
mood0.0090.0400.0710.052-0.054-0.0220.0521.0000.0840.008
osm_id0.053-0.0420.476-0.1100.240-0.1290.2840.0841.000-0.269
userid0.0240.015-0.109-0.020-0.217-0.1730.0120.008-0.2691.000