Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells9300
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

Text3
DateTime2
Numeric5
Categorical10

Alerts

gpu_model has 6673 (66.7%) missing valuesMissing
ping_ms has 189 (1.9%) missing valuesMissing
purchase_amount has 105 (1.1%) missing valuesMissing
build_version has 1654 (16.5%) missing valuesMissing
device_temp_c has 492 (4.9%) missing valuesMissing
session_id has unique valuesUnique
party_size has 213 (2.1%) zerosZeros

Reproduction

Analysis started2026-02-23 16:02:28.604376
Analysis finished2026-02-23 16:02:34.608220
Duration6 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

session_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2026-02-23T16:02:34.675977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters170000
Distinct characters18
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

Unique10000 ?
Unique (%)100.0%

Sample

1st rowsess_c2fba8e7f37a
2nd rowsess_33d286298cf9
3rd rowsess_be2bb4d8986a
4th rowsess_7f425ca9a0e2
5th rowsess_5657e28b22ec
ValueCountFrequency (%)
sess_c2fba8e7f37a1
 
< 0.1%
sess_7d5f8aacf7c51
 
< 0.1%
sess_bef87c42ebb01
 
< 0.1%
sess_be2bb4d8986a1
 
< 0.1%
sess_7f425ca9a0e21
 
< 0.1%
sess_5657e28b22ec1
 
< 0.1%
sess_acfe536087411
 
< 0.1%
sess_7280a636f1511
 
< 0.1%
sess_b196925307591
 
< 0.1%
sess_c5f6382428951
 
< 0.1%
Other values (9990)9990
99.9%
2026-02-23T16:02:34.801538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s30000
17.6%
e17570
 
10.3%
_10000
 
5.9%
47630
 
4.5%
57603
 
4.5%
87591
 
4.5%
77568
 
4.5%
07542
 
4.4%
d7531
 
4.4%
27518
 
4.4%
Other values (8)59447
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)170000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s30000
17.6%
e17570
 
10.3%
_10000
 
5.9%
47630
 
4.5%
57603
 
4.5%
87591
 
4.5%
77568
 
4.5%
07542
 
4.4%
d7531
 
4.4%
27518
 
4.4%
Other values (8)59447
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)170000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s30000
17.6%
e17570
 
10.3%
_10000
 
5.9%
47630
 
4.5%
57603
 
4.5%
87591
 
4.5%
77568
 
4.5%
07542
 
4.4%
d7531
 
4.4%
27518
 
4.4%
Other values (8)59447
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)170000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s30000
17.6%
e17570
 
10.3%
_10000
 
5.9%
47630
 
4.5%
57603
 
4.5%
87591
 
4.5%
77568
 
4.5%
07542
 
4.4%
d7531
 
4.4%
27518
 
4.4%
Other values (8)59447
35.0%

user_id
Text

Distinct2449
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2026-02-23T16:02:34.913630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.5601
Min length6

Characters and Unicode

Total characters85601
Distinct characters15
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

Unique160 ?
Unique (%)1.6%

Sample

1st rowuser_488
2nd rowuser_1511
3rd rowuser_830
4th rowuser_1
5th rowuser_211
ValueCountFrequency (%)
user_134014
 
0.1%
user_22513
 
0.1%
user_197313
 
0.1%
user_170011
 
0.1%
user_6311
 
0.1%
user_174311
 
0.1%
user_188911
 
0.1%
user_131911
 
0.1%
user_240610
 
0.1%
user_164710
 
0.1%
Other values (2439)9885
98.9%
2026-02-23T16:02:35.078544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u10000
11.7%
s10000
11.7%
e10000
11.7%
r10000
11.7%
_10000
11.7%
17229
8.4%
25161
 
6.0%
43206
 
3.7%
33191
 
3.7%
82824
 
3.3%
Other values (5)13990
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)85601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u10000
11.7%
s10000
11.7%
e10000
11.7%
r10000
11.7%
_10000
11.7%
17229
8.4%
25161
 
6.0%
43206
 
3.7%
33191
 
3.7%
82824
 
3.3%
Other values (5)13990
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)85601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u10000
11.7%
s10000
11.7%
e10000
11.7%
r10000
11.7%
_10000
11.7%
17229
8.4%
25161
 
6.0%
43206
 
3.7%
33191
 
3.7%
82824
 
3.3%
Other values (5)13990
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)85601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u10000
11.7%
s10000
11.7%
e10000
11.7%
r10000
11.7%
_10000
11.7%
17229
8.4%
25161
 
6.0%
43206
 
3.7%
33191
 
3.7%
82824
 
3.3%
Other values (5)13990
16.3%
Distinct9948
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2025-06-13 23:21:08+00:00
Maximum2025-07-18 18:32:00+00:00
Invalid dates9998
Invalid dates (%)> 99.9%
2026-02-23T16:02:35.118262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:35.159220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
Distinct9852
Distinct (%)99.4%
Missing89
Missing (%)0.9%
Memory size78.3 KiB
Minimum2025-07-18 20:03:21-05:00
Maximum2025-07-18 20:03:21-05:00
Invalid dates9910
Invalid dates (%)99.1%
2026-02-23T16:02:35.199449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:35.240449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

session_length_s
Real number (ℝ)

Distinct5428
Distinct (%)54.8%
Missing98
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean5616.2443
Minimum-16000
Maximum704200
Zeros0
Zeros (%)0.0%
Negative139
Negative (%)1.4%
Memory size78.3 KiB
2026-02-23T16:02:35.298146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-16000
5-th percentile87.05
Q11634.25
median3512
Q35387.5
95-th percentile6860.95
Maximum704200
Range720200
Interquartile range (IQR)3753.25

Descriptive statistics

Standard deviation33635.442
Coefficient of variation (CV)5.9889564
Kurtosis288.09018
Mean5616.2443
Median Absolute Deviation (MAD)1877
Skewness16.635454
Sum55612051
Variance1.131343 × 109
MonotonicityNot monotonic
2026-02-23T16:02:35.362406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9211
 
0.1%
6011
 
0.1%
948
 
0.1%
158
 
0.1%
548
 
0.1%
1148
 
0.1%
1188
 
0.1%
1008
 
0.1%
707
 
0.1%
937
 
0.1%
Other values (5418)9818
98.2%
(Missing)98
 
1.0%
ValueCountFrequency (%)
-160001
< 0.1%
-71451
< 0.1%
-68031
< 0.1%
-67771
< 0.1%
-67431
< 0.1%
-65721
< 0.1%
-65181
< 0.1%
-63041
< 0.1%
-61551
< 0.1%
-60731
< 0.1%
ValueCountFrequency (%)
7042001
< 0.1%
6965001
< 0.1%
6911001
< 0.1%
6777001
< 0.1%
6769001
< 0.1%
6727001
< 0.1%
6677001
< 0.1%
6649001
< 0.1%
6502001
< 0.1%
6234001
< 0.1%

region
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
EU
1207 
sa-east-1
1193 
APAC
1161 
us-east
1160 
Us-West
803 
Other values (27)
4476 

Length

Max length11
Median length9
Mean length5.9674
Min length2

Characters and Unicode

Total characters59674
Distinct characters19
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 rowus-west
2nd rowUs-east
3rd rowsa-east-1
4th rowsa-east-1
5th rowUS-EAST

Common Values

ValueCountFrequency (%)
EU1207
12.1%
sa-east-11193
11.9%
APAC1161
11.6%
us-east1160
11.6%
Us-West803
 
8.0%
us-west404
 
4.0%
apac375
 
3.8%
eu373
 
3.7%
US-EAST336
 
3.4%
SA-EAST-1335
 
3.4%
Other values (22)2653
26.5%

Length

2026-02-23T16:02:35.420133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eu2047
20.5%
apac2003
20.0%
us-west1999
20.0%
sa-east-11987
19.9%
us-east1964
19.6%

Most occurring characters

ValueCountFrequency (%)
s9377
15.7%
-7937
13.3%
a5954
10.0%
e5252
8.8%
t4843
8.1%
A3990
 
6.7%
U3578
 
6.0%
E2745
 
4.6%
S2523
 
4.2%
u2432
 
4.1%
Other values (9)11043
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)59674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s9377
15.7%
-7937
13.3%
a5954
10.0%
e5252
8.8%
t4843
8.1%
A3990
 
6.7%
U3578
 
6.0%
E2745
 
4.6%
S2523
 
4.2%
u2432
 
4.1%
Other values (9)11043
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)59674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s9377
15.7%
-7937
13.3%
a5954
10.0%
e5252
8.8%
t4843
8.1%
A3990
 
6.7%
U3578
 
6.0%
E2745
 
4.6%
S2523
 
4.2%
u2432
 
4.1%
Other values (9)11043
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)59674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s9377
15.7%
-7937
13.3%
a5954
10.0%
e5252
8.8%
t4843
8.1%
A3990
 
6.7%
U3578
 
6.0%
E2745
 
4.6%
S2523
 
4.2%
u2432
 
4.1%
Other values (9)11043
18.5%

platform
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
PC
1294 
pc
1293 
Switch
922 
Mobile
905 
Xbox
880 
Other values (27)
4706 

Length

Max length13
Median length11
Mean length5.3908
Min length2

Characters and Unicode

Total characters53908
Distinct characters33
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 rowpc
2nd rowPlayStation
3rd rowPlayStation
4th rowPlayStation
5th rowswitch

Common Values

ValueCountFrequency (%)
PC1294
12.9%
pc1293
12.9%
Switch922
 
9.2%
Mobile905
 
9.0%
Xbox880
 
8.8%
PlayStation730
 
7.3%
Pc426
 
4.3%
PLAYSTATION307
 
3.1%
xbox305
 
3.0%
SWITCH303
 
3.0%
Other values (22)2635
26.4%

Length

2026-02-23T16:02:35.472236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pc3327
33.3%
playstation1720
17.2%
switch1668
16.7%
xbox1655
16.6%
mobile1630
16.3%

Most occurring characters

ValueCountFrequency (%)
t4086
 
7.6%
i4009
 
7.4%
o3996
 
7.4%
P3293
 
6.1%
c3243
 
6.0%
a2752
 
5.1%
l2675
 
5.0%
b2620
 
4.9%
S2496
 
4.6%
1926
 
3.6%
Other values (23)22812
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)53908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t4086
 
7.6%
i4009
 
7.4%
o3996
 
7.4%
P3293
 
6.1%
c3243
 
6.0%
a2752
 
5.1%
l2675
 
5.0%
b2620
 
4.9%
S2496
 
4.6%
1926
 
3.6%
Other values (23)22812
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)53908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t4086
 
7.6%
i4009
 
7.4%
o3996
 
7.4%
P3293
 
6.1%
c3243
 
6.0%
a2752
 
5.1%
l2675
 
5.0%
b2620
 
4.9%
S2496
 
4.6%
1926
 
3.6%
Other values (23)22812
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)53908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t4086
 
7.6%
i4009
 
7.4%
o3996
 
7.4%
P3293
 
6.1%
c3243
 
6.0%
a2752
 
5.1%
l2675
 
5.0%
b2620
 
4.9%
S2496
 
4.6%
1926
 
3.6%
Other values (23)22812
42.3%

gpu_model
Categorical

Missing 

Distinct6
Distinct (%)0.2%
Missing6673
Missing (%)66.7%
Memory size78.3 KiB
GeForce RTX-3060
581 
GTX1080
568 
RTX-4090
558 
RTX 3060
556 
Intel Iris
541 

Length

Max length16
Median length10
Mean length9.3943493
Min length7

Characters and Unicode

Total characters31255
Distinct characters24
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 rowGTX1080
2nd rowRTX 3060
3rd rowRTX 3060
4th rowGTX1080
5th rowGTX1080

Common Values

ValueCountFrequency (%)
GeForce RTX-3060581
 
5.8%
GTX1080568
 
5.7%
RTX-4090558
 
5.6%
RTX 3060556
 
5.6%
Intel Iris541
 
5.4%
RX 6600523
 
5.2%
(Missing)6673
66.7%

Length

2026-02-23T16:02:35.517122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:02:35.563672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
geforce581
10.5%
rtx-3060581
10.5%
gtx1080568
10.3%
rtx-4090558
10.1%
rtx556
10.1%
3060556
10.1%
intel541
9.8%
iris541
9.8%
rx523
9.5%
6600523
9.5%

Most occurring characters

ValueCountFrequency (%)
05572
17.8%
X2786
 
8.9%
T2263
 
7.2%
R2218
 
7.1%
2201
 
7.0%
62183
 
7.0%
e1703
 
5.4%
G1149
 
3.7%
-1139
 
3.6%
31137
 
3.6%
Other values (14)8904
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)31255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05572
17.8%
X2786
 
8.9%
T2263
 
7.2%
R2218
 
7.1%
2201
 
7.0%
62183
 
7.0%
e1703
 
5.4%
G1149
 
3.7%
-1139
 
3.6%
31137
 
3.6%
Other values (14)8904
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05572
17.8%
X2786
 
8.9%
T2263
 
7.2%
R2218
 
7.1%
2201
 
7.0%
62183
 
7.0%
e1703
 
5.4%
G1149
 
3.7%
-1139
 
3.6%
31137
 
3.6%
Other values (14)8904
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05572
17.8%
X2786
 
8.9%
T2263
 
7.2%
R2218
 
7.1%
2201
 
7.0%
62183
 
7.0%
e1703
 
5.4%
G1149
 
3.7%
-1139
 
3.6%
31137
 
3.6%
Other values (14)8904
28.5%

avg_fps
Real number (ℝ)

Distinct5848
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.039898
Minimum5
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-23T16:02:35.628185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile31.7195
Q150.54
median64.57
Q381.7125
95-th percentile105.18
Maximum10000
Range9995
Interquartile range (IQR)31.1725

Descriptive statistics

Standard deviation515.97692
Coefficient of variation (CV)5.5457597
Kurtosis364.20394
Mean93.039898
Median Absolute Deviation (MAD)15.43
Skewness19.116797
Sum930398.98
Variance266232.18
MonotonicityNot monotonic
2026-02-23T16:02:35.687054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000027
 
0.3%
58
 
0.1%
81.997
 
0.1%
54.787
 
0.1%
61.537
 
0.1%
49.587
 
0.1%
46.957
 
0.1%
68.287
 
0.1%
60.337
 
0.1%
43.317
 
0.1%
Other values (5838)9909
99.1%
ValueCountFrequency (%)
58
0.1%
6.031
 
< 0.1%
6.571
 
< 0.1%
7.41
 
< 0.1%
7.491
 
< 0.1%
7.531
 
< 0.1%
7.911
 
< 0.1%
8.021
 
< 0.1%
8.421
 
< 0.1%
8.51
 
< 0.1%
ValueCountFrequency (%)
1000027
0.3%
136.161
 
< 0.1%
133.361
 
< 0.1%
133.061
 
< 0.1%
132.981
 
< 0.1%
131.961
 
< 0.1%
130.761
 
< 0.1%
130.621
 
< 0.1%
130.431
 
< 0.1%
129.941
 
< 0.1%

ping_ms
Real number (ℝ)

Missing 

Distinct6411
Distinct (%)65.3%
Missing189
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean54.824352
Minimum0
Maximum627.05
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-23T16:02:35.756449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.395
Q130.135
median50.27
Q370.885
95-th percentile103.1
Maximum627.05
Range627.05
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation46.701765
Coefficient of variation (CV)0.85184345
Kurtosis45.603069
Mean54.824352
Median Absolute Deviation (MAD)20.32
Skewness5.4014407
Sum537881.72
Variance2181.0549
MonotonicityNot monotonic
2026-02-23T16:02:35.821963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.957
 
0.1%
34.996
 
0.1%
30.396
 
0.1%
48.866
 
0.1%
36.86
 
0.1%
23.686
 
0.1%
29.985
 
0.1%
54.695
 
0.1%
48.445
 
0.1%
58.795
 
0.1%
Other values (6401)9754
97.5%
(Missing)189
 
1.9%
ValueCountFrequency (%)
01
 
< 0.1%
0.021
 
< 0.1%
0.032
< 0.1%
0.042
< 0.1%
0.073
< 0.1%
0.111
 
< 0.1%
0.131
 
< 0.1%
0.152
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
ValueCountFrequency (%)
627.051
< 0.1%
592.661
< 0.1%
589.671
< 0.1%
588.811
< 0.1%
556.281
< 0.1%
549.141
< 0.1%
543.741
< 0.1%
542.891
< 0.1%
542.051
< 0.1%
540.231
< 0.1%

map_name
Categorical

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
ocean
1019 
desert
984 
cave
951 
CITY-XL
951 
Forest
907 
Other values (31)
5188 

Length

Max length9
Median length8
Mean length6.0215
Min length4

Characters and Unicode

Total characters60215
Distinct characters33
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 rowocean
2nd rowdesert
3rd rowForest
4th rowDesert
5th rowDesert

Common Values

ValueCountFrequency (%)
ocean1019
 
10.2%
desert984
 
9.8%
cave951
 
9.5%
CITY-XL951
 
9.5%
Forest907
 
9.1%
Arena-1871
 
8.7%
FOREST315
 
3.1%
OCEAN312
 
3.1%
arena-1308
 
3.1%
forest303
 
3.0%
Other values (26)3079
30.8%

Length

2026-02-23T16:02:35.883505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ocean1725
17.2%
desert1695
17.0%
forest1694
16.9%
city-xl1634
16.3%
cave1632
16.3%
arena-11620
16.2%

Most occurring characters

ValueCountFrequency (%)
e8029
 
13.3%
a4313
 
7.2%
r4001
 
6.6%
t3264
 
5.4%
-3254
 
5.4%
c2730
 
4.5%
s2705
 
4.5%
n2672
 
4.4%
o2466
 
4.1%
A2284
 
3.8%
Other values (23)24497
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8029
 
13.3%
a4313
 
7.2%
r4001
 
6.6%
t3264
 
5.4%
-3254
 
5.4%
c2730
 
4.5%
s2705
 
4.5%
n2672
 
4.4%
o2466
 
4.1%
A2284
 
3.8%
Other values (23)24497
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8029
 
13.3%
a4313
 
7.2%
r4001
 
6.6%
t3264
 
5.4%
-3254
 
5.4%
c2730
 
4.5%
s2705
 
4.5%
n2672
 
4.4%
o2466
 
4.1%
A2284
 
3.8%
Other values (23)24497
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8029
 
13.3%
a4313
 
7.2%
r4001
 
6.6%
t3264
 
5.4%
-3254
 
5.4%
c2730
 
4.5%
s2705
 
4.5%
n2672
 
4.4%
o2466
 
4.1%
A2284
 
3.8%
Other values (23)24497
40.7%

crash_flag
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
False
2398 
No
2309 
0
2297 
false
2178 
Yes
 
224
Other values (3)
594 

Length

Max length5
Median length4
Mean length3.2252
Min length1

Characters and Unicode

Total characters32252
Distinct characters15
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 rowYes
2nd rowNo
3rd rowFalse
4th rowNo
5th rowFalse

Common Values

ValueCountFrequency (%)
False2398
24.0%
No2309
23.1%
02297
23.0%
false2178
21.8%
Yes224
 
2.2%
True205
 
2.1%
1197
 
2.0%
true192
 
1.9%

Length

2026-02-23T16:02:35.938599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:02:35.988711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
false4576
45.8%
no2309
23.1%
02297
23.0%
true397
 
4.0%
yes224
 
2.2%
1197
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e5197
16.1%
s4800
14.9%
a4576
14.2%
l4576
14.2%
F2398
7.4%
N2309
7.2%
o2309
7.2%
02297
7.1%
f2178
6.8%
r397
 
1.2%
Other values (5)1215
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)32252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5197
16.1%
s4800
14.9%
a4576
14.2%
l4576
14.2%
F2398
7.4%
N2309
7.2%
o2309
7.2%
02297
7.1%
f2178
6.8%
r397
 
1.2%
Other values (5)1215
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5197
16.1%
s4800
14.9%
a4576
14.2%
l4576
14.2%
F2398
7.4%
N2309
7.2%
o2309
7.2%
02297
7.1%
f2178
6.8%
r397
 
1.2%
Other values (5)1215
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5197
16.1%
s4800
14.9%
a4576
14.2%
l4576
14.2%
F2398
7.4%
N2309
7.2%
o2309
7.2%
02297
7.1%
f2178
6.8%
r397
 
1.2%
Other values (5)1215
 
3.8%

purchase_amount
Text

Missing 

Distinct1725
Distinct (%)17.4%
Missing105
Missing (%)1.1%
Memory size78.3 KiB
2026-02-23T16:02:36.116677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.4385043
Min length3

Characters and Unicode

Total characters34024
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

Unique1089 ?
Unique (%)11.0%

Sample

1st row0,00
2nd row0.0
3rd row0.0
4th row17.55
5th row0.0
ValueCountFrequency (%)
0.06211
62.8%
0,00851
 
8.6%
2.429
 
0.1%
0.447
 
0.1%
0.737
 
0.1%
4.097
 
0.1%
0.967
 
0.1%
7.297
 
0.1%
6.067
 
0.1%
7.417
 
0.1%
Other values (1641)2775
28.0%
2026-02-23T16:02:36.292625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
015777
46.4%
.8696
25.6%
11549
 
4.6%
,1199
 
3.5%
21117
 
3.3%
3916
 
2.7%
4875
 
2.6%
5827
 
2.4%
7776
 
2.3%
6746
 
2.2%
Other values (3)1546
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)34024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015777
46.4%
.8696
25.6%
11549
 
4.6%
,1199
 
3.5%
21117
 
3.3%
3916
 
2.7%
4875
 
2.6%
5827
 
2.4%
7776
 
2.3%
6746
 
2.2%
Other values (3)1546
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015777
46.4%
.8696
25.6%
11549
 
4.6%
,1199
 
3.5%
21117
 
3.3%
3916
 
2.7%
4875
 
2.6%
5827
 
2.4%
7776
 
2.3%
6746
 
2.2%
Other values (3)1546
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015777
46.4%
.8696
25.6%
11549
 
4.6%
,1199
 
3.5%
21117
 
3.3%
3916
 
2.7%
4875
 
2.6%
5827
 
2.4%
7776
 
2.3%
6746
 
2.2%
Other values (3)1546
 
4.5%

party_size
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.231
Minimum0
Maximum9
Zeros213
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-23T16:02:36.338115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5605392
Coefficient of variation (CV)0.69947969
Kurtosis4.6794584
Mean2.231
Median Absolute Deviation (MAD)1
Skewness1.7722592
Sum22310
Variance2.4352825
MonotonicityNot monotonic
2026-02-23T16:02:36.374998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13949
39.5%
22448
24.5%
31609
16.1%
41023
 
10.2%
5569
 
5.7%
0213
 
2.1%
9189
 
1.9%
ValueCountFrequency (%)
0213
 
2.1%
13949
39.5%
22448
24.5%
31609
16.1%
41023
 
10.2%
5569
 
5.7%
9189
 
1.9%
ValueCountFrequency (%)
9189
 
1.9%
5569
 
5.7%
41023
 
10.2%
31609
16.1%
22448
24.5%
13949
39.5%
0213
 
2.1%

input_method
Categorical

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
controllr
1196 
kbm
1187 
KB/M
1165 
touch
1159 
controller
1125 
Other values (25)
4168 

Length

Max length12
Median length10
Mean length6.3857
Min length3

Characters and Unicode

Total characters63857
Distinct characters26
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 rowTouch
2nd rowTouch
3rd rowTOUCH
4th rowController
5th rowcontrollr

Common Values

ValueCountFrequency (%)
controllr1196
12.0%
kbm1187
11.9%
KB/M1165
11.7%
touch1159
11.6%
controller1125
11.2%
KBM383
 
3.8%
TOUCH380
 
3.8%
kb/m351
 
3.5%
CONTROLLER348
 
3.5%
CONTROLLR340
 
3.4%
Other values (20)2366
23.7%

Length

2026-02-23T16:02:36.422321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kbm2024
20.2%
touch2010
20.1%
controllr2007
20.1%
controller1982
19.8%
kb/m1977
19.8%

Most occurring characters

ValueCountFrequency (%)
o8028
 
12.6%
r6428
 
10.1%
l6428
 
10.1%
t4515
 
7.1%
c4209
 
6.6%
n3214
 
5.0%
K2311
 
3.6%
b2309
 
3.6%
m2309
 
3.6%
/1977
 
3.1%
Other values (16)22129
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)63857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o8028
 
12.6%
r6428
 
10.1%
l6428
 
10.1%
t4515
 
7.1%
c4209
 
6.6%
n3214
 
5.0%
K2311
 
3.6%
b2309
 
3.6%
m2309
 
3.6%
/1977
 
3.1%
Other values (16)22129
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o8028
 
12.6%
r6428
 
10.1%
l6428
 
10.1%
t4515
 
7.1%
c4209
 
6.6%
n3214
 
5.0%
K2311
 
3.6%
b2309
 
3.6%
m2309
 
3.6%
/1977
 
3.1%
Other values (16)22129
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o8028
 
12.6%
r6428
 
10.1%
l6428
 
10.1%
t4515
 
7.1%
c4209
 
6.6%
n3214
 
5.0%
K2311
 
3.6%
b2309
 
3.6%
m2309
 
3.6%
/1977
 
3.1%
Other values (16)22129
34.7%

build_version
Categorical

Missing 

Distinct5
Distinct (%)0.1%
Missing1654
Missing (%)16.5%
Memory size78.3 KiB
1.3.2
1705 
latest
1695 
1.4
1667 
v1.3.2
1648 
1.4.0-beta
1631 

Length

Max length10
Median length6
Mean length5.9781931
Min length3

Characters and Unicode

Total characters49894
Distinct characters14
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 row1.4
2nd row1.3.2
3rd rowlatest
4th row1.4.0-beta
5th row1.4

Common Values

ValueCountFrequency (%)
1.3.21705
17.1%
latest1695
17.0%
1.41667
16.7%
v1.3.21648
16.5%
1.4.0-beta1631
16.3%
(Missing)1654
16.5%

Length

2026-02-23T16:02:36.471229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:02:36.512302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.3.21705
20.4%
latest1695
20.3%
1.41667
20.0%
v1.3.21648
19.7%
1.4.0-beta1631
19.5%

Most occurring characters

ValueCountFrequency (%)
.11635
23.3%
16651
13.3%
t5021
10.1%
33353
 
6.7%
23353
 
6.7%
a3326
 
6.7%
e3326
 
6.7%
43298
 
6.6%
l1695
 
3.4%
s1695
 
3.4%
Other values (4)6541
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)49894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.11635
23.3%
16651
13.3%
t5021
10.1%
33353
 
6.7%
23353
 
6.7%
a3326
 
6.7%
e3326
 
6.7%
43298
 
6.6%
l1695
 
3.4%
s1695
 
3.4%
Other values (4)6541
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.11635
23.3%
16651
13.3%
t5021
10.1%
33353
 
6.7%
23353
 
6.7%
a3326
 
6.7%
e3326
 
6.7%
43298
 
6.6%
l1695
 
3.4%
s1695
 
3.4%
Other values (4)6541
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.11635
23.3%
16651
13.3%
t5021
10.1%
33353
 
6.7%
23353
 
6.7%
a3326
 
6.7%
e3326
 
6.7%
43298
 
6.6%
l1695
 
3.4%
s1695
 
3.4%
Other values (4)6541
13.1%

is_featured_event
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
False
2430 
false
2369 
No
2337 
0
2331 
1
 
144
Other values (3)
389 

Length

Max length5
Median length4
Mean length3.2569
Min length1

Characters and Unicode

Total characters32569
Distinct characters15
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 rowNo
2nd row0
3rd rowFalse
4th row0
5th row0

Common Values

ValueCountFrequency (%)
False2430
24.3%
false2369
23.7%
No2337
23.4%
02331
23.3%
1144
 
1.4%
True133
 
1.3%
Yes131
 
1.3%
true125
 
1.2%

Length

2026-02-23T16:02:36.567681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:02:36.615374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
false4799
48.0%
no2337
23.4%
02331
23.3%
true258
 
2.6%
1144
 
1.4%
yes131
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e5188
15.9%
s4930
15.1%
a4799
14.7%
l4799
14.7%
F2430
7.5%
f2369
7.3%
N2337
7.2%
o2337
7.2%
02331
7.2%
r258
 
0.8%
Other values (5)791
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)32569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5188
15.9%
s4930
15.1%
a4799
14.7%
l4799
14.7%
F2430
7.5%
f2369
7.3%
N2337
7.2%
o2337
7.2%
02331
7.2%
r258
 
0.8%
Other values (5)791
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5188
15.9%
s4930
15.1%
a4799
14.7%
l4799
14.7%
F2430
7.5%
f2369
7.3%
N2337
7.2%
o2337
7.2%
02331
7.2%
r258
 
0.8%
Other values (5)791
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5188
15.9%
s4930
15.1%
a4799
14.7%
l4799
14.7%
F2430
7.5%
f2369
7.3%
N2337
7.2%
o2337
7.2%
02331
7.2%
r258
 
0.8%
Other values (5)791
 
2.4%

device_temp_c
Real number (ℝ)

Missing 

Distinct549
Distinct (%)5.8%
Missing492
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean55.778313
Minimum26.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-23T16:02:36.675432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.3
5-th percentile43.7
Q150.5
median55.2
Q360.3
95-th percentile68.6
Maximum100
Range73.7
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation8.3458191
Coefficient of variation (CV)0.1496248
Kurtosis2.8152031
Mean55.778313
Median Absolute Deviation (MAD)4.9
Skewness0.96683709
Sum530340.2
Variance69.652697
MonotonicityNot monotonic
2026-02-23T16:02:36.736443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.170
 
0.7%
54.466
 
0.7%
54.266
 
0.7%
54.865
 
0.7%
51.965
 
0.7%
53.465
 
0.7%
53.563
 
0.6%
5361
 
0.6%
56.161
 
0.6%
56.961
 
0.6%
Other values (539)8865
88.6%
(Missing)492
 
4.9%
ValueCountFrequency (%)
26.31
< 0.1%
27.41
< 0.1%
30.41
< 0.1%
30.51
< 0.1%
311
< 0.1%
31.71
< 0.1%
32.61
< 0.1%
32.71
< 0.1%
331
< 0.1%
33.11
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
98.71
 
< 0.1%
981
 
< 0.1%
96.81
 
< 0.1%
95.41
 
< 0.1%
94.52
< 0.1%
94.23
< 0.1%
93.41
 
< 0.1%
93.21
 
< 0.1%
931
 
< 0.1%

session_type
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
ranked
2807 
casual
2670 
Casual
919 
RANKED
906 
CASUAL
860 
Other values (7)
1838 

Length

Max length8
Median length6
Mean length6.1982
Min length6

Characters and Unicode

Total characters61982
Distinct characters21
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 rowranked
2nd rowcasual
3rd rowranked
4th rowcasual
5th rowcasual

Common Values

ValueCountFrequency (%)
ranked2807
28.1%
casual2670
26.7%
Casual919
 
9.2%
RANKED906
 
9.1%
CASUAL860
 
8.6%
Ranked847
 
8.5%
casual306
 
3.1%
ranked295
 
2.9%
Ranked107
 
1.1%
CASUAL103
 
1.0%
Other values (2)180
 
1.8%

Length

2026-02-23T16:02:36.793778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ranked5058
50.6%
casual4942
49.4%

Most occurring characters

ValueCountFrequency (%)
a12014
19.4%
n4056
 
6.5%
k4056
 
6.5%
e4056
 
6.5%
d4056
 
6.5%
s3979
 
6.4%
u3979
 
6.4%
l3979
 
6.4%
r3102
 
5.0%
c2976
 
4.8%
Other values (11)15729
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)61982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a12014
19.4%
n4056
 
6.5%
k4056
 
6.5%
e4056
 
6.5%
d4056
 
6.5%
s3979
 
6.4%
u3979
 
6.4%
l3979
 
6.4%
r3102
 
5.0%
c2976
 
4.8%
Other values (11)15729
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a12014
19.4%
n4056
 
6.5%
k4056
 
6.5%
e4056
 
6.5%
d4056
 
6.5%
s3979
 
6.4%
u3979
 
6.4%
l3979
 
6.4%
r3102
 
5.0%
c2976
 
4.8%
Other values (11)15729
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a12014
19.4%
n4056
 
6.5%
k4056
 
6.5%
e4056
 
6.5%
d4056
 
6.5%
s3979
 
6.4%
u3979
 
6.4%
l3979
 
6.4%
r3102
 
5.0%
c2976
 
4.8%
Other values (11)15729
25.4%

is_long_session
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Yes
1533 
True
1500 
1
1494 
true
1472 
False
1022 
Other values (3)
2979 

Length

Max length5
Median length4
Mean length3.1058
Min length1

Characters and Unicode

Total characters31058
Distinct characters15
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 rowTrue
2nd row0
3rd rowFalse
4th rowTrue
5th rowYes

Common Values

ValueCountFrequency (%)
Yes1533
15.3%
True1500
15.0%
11494
14.9%
true1472
14.7%
False1022
10.2%
false1001
10.0%
0994
9.9%
No984
9.8%

Length

2026-02-23T16:02:36.847794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:02:36.896267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
true2972
29.7%
false2023
20.2%
yes1533
15.3%
11494
14.9%
0994
 
9.9%
no984
 
9.8%

Most occurring characters

ValueCountFrequency (%)
e6528
21.0%
s3556
11.4%
r2972
9.6%
u2972
9.6%
a2023
 
6.5%
l2023
 
6.5%
Y1533
 
4.9%
T1500
 
4.8%
11494
 
4.8%
t1472
 
4.7%
Other values (5)4985
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)31058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e6528
21.0%
s3556
11.4%
r2972
9.6%
u2972
9.6%
a2023
 
6.5%
l2023
 
6.5%
Y1533
 
4.9%
T1500
 
4.8%
11494
 
4.8%
t1472
 
4.7%
Other values (5)4985
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e6528
21.0%
s3556
11.4%
r2972
9.6%
u2972
9.6%
a2023
 
6.5%
l2023
 
6.5%
Y1533
 
4.9%
T1500
 
4.8%
11494
 
4.8%
t1472
 
4.7%
Other values (5)4985
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e6528
21.0%
s3556
11.4%
r2972
9.6%
u2972
9.6%
a2023
 
6.5%
l2023
 
6.5%
Y1533
 
4.9%
T1500
 
4.8%
11494
 
4.8%
t1472
 
4.7%
Other values (5)4985
16.1%

Interactions

2026-02-23T16:02:33.896661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:32.915206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.185228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.417839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.667095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.946776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:32.977553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.224962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.467292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.718297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.999020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.031110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.279476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.526962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.756046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:34.047027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.085688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.326133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.573769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.808213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:34.097368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.132193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.369027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.616436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:02:33.847944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-23T16:02:36.958252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_fpsbuild_versioncrash_flagdevice_temp_cgpu_modelinput_methodis_featured_eventis_long_sessionmap_nameparty_sizeping_msplatformregionsession_length_ssession_type
avg_fps1.0000.0220.0000.0090.0000.0140.0230.0180.000-0.0120.0020.0000.000-0.0180.000
build_version0.0221.0000.0100.0000.0000.0060.0190.0190.0000.0000.0000.0050.0130.0000.000
crash_flag0.0000.0101.0000.0000.0000.0000.0150.0150.0000.0000.0000.0020.0190.0000.000
device_temp_c0.0090.0000.0001.0000.0190.0130.0240.0000.0150.0080.0060.0240.007-0.0050.000
gpu_model0.0000.0000.0000.0191.0000.0000.0000.0000.0000.0160.0000.0210.0000.0000.000
input_method0.0140.0060.0000.0130.0001.0000.0130.0180.0010.0100.0130.0000.0050.0000.020
is_featured_event0.0230.0190.0150.0240.0000.0131.0000.0000.0150.0000.0150.0000.0110.0180.012
is_long_session0.0180.0190.0150.0000.0000.0180.0001.0000.0000.0120.0040.0080.0110.0000.012
map_name0.0000.0000.0000.0150.0000.0010.0150.0001.0000.0000.0000.0040.0170.0000.000
party_size-0.0120.0000.0000.0080.0160.0100.0000.0120.0001.000-0.0040.0000.0230.0270.000
ping_ms0.0020.0000.0000.0060.0000.0130.0150.0040.000-0.0041.0000.0000.000-0.0010.000
platform0.0000.0050.0020.0240.0210.0000.0000.0080.0040.0000.0001.0000.0090.0130.010
region0.0000.0130.0190.0070.0000.0050.0110.0110.0170.0230.0000.0091.0000.0000.000
session_length_s-0.0180.0000.000-0.0050.0000.0000.0180.0000.0000.027-0.0010.0130.0001.0000.025
session_type0.0000.0000.0000.0000.0000.0200.0120.0120.0000.0000.0000.0100.0000.0251.000

Missing values

2026-02-23T16:02:34.195568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-23T16:02:34.315497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-23T16:02:34.549653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

session_iduser_idstart_timeend_timesession_length_sregionplatformgpu_modelavg_fpsping_msmap_namecrash_flagpurchase_amountparty_sizeinput_methodbuild_versionis_featured_eventdevice_temp_csession_typeis_long_session
0sess_c2fba8e7f37auser_4882025-07-18T18:32:00Z2025-07-18 20:03:21-05:005481.0us-westpcGTX108083.52431.16oceanYes0,002TouchNaNNo85.6rankedTrue
1sess_33d286298cf9user_15112025-06-13 23:21:08+00:002025-06-13 23:36:30+01:00922.0Us-eastPlayStationNaN72.7529.12desertNo0.03TouchNaN062.0casual0
2sess_be2bb4d8986auser_8302025-10-20 02:42:07-05:0020/10/2025 02:49451.0sa-east-1PlayStationNaN69.2040.47ForestFalse0.05TOUCH1.4False69.0rankedFalse
3sess_7f425ca9a0e2user_108/01/2025 06:352025-08-01T08:32:45Z7031.0sa-east-1PlayStationNaN33.2992.40DesertNo17.551Controller1.3.2048.1casualTrue
4sess_5657e28b22ecuser_2112025-09-08T23:41:44Z2025-09-09 00:32:59+01:003075.0US-EASTswitchNaN69.9612.63DesertFalse0.02controllrNaN054.7casualYes
5sess_acfe53608741user_174312/04/2025 02:4604/12/2025 03:00824.0ApacswitchNaN50.7172.42ARENA-1No0.02controllrlatestfalseNaNcasualNo
6sess_7280a636f151user_23062025-03-21 05:33:04+00:002025-03-21T07:22:46Z6582.0eupcRTX 306049.13100.26Arena-1No0.02CONTROLLR1.4.0-beta0NaNranked1
7sess_b19692530759user_49001/12/2025 11:5401/12/2025 13:05428600.0EUPcRTX 306064.436.16ForestNo1,802Touch1.4false54.7RANKEDtrue
8sess_7d5f8aacf7c5user_222410/30/2025 18:5830/10/2025 19:362268.0Us-eastswitchNaN57.5361.56Arena-100.01controllerv1.3.2049.5RANKED0
9sess_c5f638242895user_224815/12/2025 08:512025-12-15 10:41:52+00:006640.0us-eastPCGTX108079.8368.36CAVEfalse7.851controllerlatestfalse47.6casualtrue
session_iduser_idstart_timeend_timesession_length_sregionplatformgpu_modelavg_fpsping_msmap_namecrash_flagpurchase_amountparty_sizeinput_methodbuild_versionis_featured_eventdevice_temp_csession_typeis_long_session
9990sess_3ada82923aceuser_6321/03/2025 17:3403/21/2025 18:303358.0Us-WestXboxNaN50.1963.47ARENA-1false0,001KB/M1.3.2False56.7ranked1
9991sess_d7cdf1cd0cdduser_14010/23/2025 14:012025-10-23T15:03:15Z3731.0us-westPCRX 660067.6059.72FOREST00.03KB/MlatestFalse56.7casual1
9992sess_4223eb96333euser_207207/29/2025 12:472025-07-29 14:35:50+00:006512.0sa-east-1PCIntel Iris67.8518.87Cavetrue3.41touch1.4.0-betaFalse59.2CASUAL1
9993sess_d828a4c9f797user_109106/24/2025 02:412025-06-24 03:45:38-05:003861.0sa-east-1MOBILENaN81.1632.12desertFalse0.01controllrlatestTrue56.6rankedtrue
9994sess_26ccae387a60user_8812025-07-01 20:33:24+00:0001/07/2025 20:48883.0Us-WestXBOXNaN60.2665.22oceanFalse0.02KB/M1.3.2063.8casualfalse
9995sess_41b176ee9033user_128928/01/2025 10:482025-01-28 12:37:18+01:00650200.0EUPCIntel Iris56.89288.07ForestNo0.01kb/m1.3.2false55.0casual1
9996sess_7d987f8e3441user_22362025-04-17T20:10:25Z2025-04-17 20:31:30-05:001265.0APACmobileNaN84.8433.16Forest07.091Kbm1.4False63.6ranked0
9997sess_d09e63bc199fuser_3522025-10-22 04:08:19-05:002025-10-22T04:40:14Z1915.0EUSwitchNaN55.3122.83CITY-XL00,000controllerNaNFalse48.2rankedNo
9998sess_c14b97875413user_11212025-10-20T12:02:48Z10/20/2025 12:593373.0sa-east-1pcRTX-409093.6378.64desertNo0.02touch1.3.2058.5rankedTrue
9999sess_dfbe04022bf9user_221401/03/2025 14:3101/03/2025 15:504739.0sa-east-1PCGeForce RTX-306093.6160.31forestNo2.425KBMv1.3.2No66.3casual1