Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations41002
Missing cells8591
Missing cells (%)2.3%
Duplicate rows45
Duplicate rows (%)0.1%
Total size in memory18.4 MiB
Average record size in memory469.8 B

Variable types

Text6
Categorical2
Numeric1

Alerts

Dataset has 45 (0.1%) duplicate rowsDuplicates
Name has 850 (2.1%) missing valuesMissing
Opponent has 960 (2.3%) missing valuesMissing
W/L has 850 (2.1%) missing valuesMissing
Method has 1862 (4.5%) missing valuesMissing
Competition has 870 (2.1%) missing valuesMissing
Weight has 1301 (3.2%) missing valuesMissing
Stage has 1048 (2.6%) missing valuesMissing
Year has 850 (2.1%) missing valuesMissing

Reproduction

Analysis started2024-07-28 16:11:17.272738
Analysis finished2024-07-28 16:11:19.436010
Duration2.16 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

URL Tag
Text

Distinct1374
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-07-28T10:11:19.604198image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length27
Median length25
Mean length13.50361
Min length6

Characters and Unicode

Total characters553675
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique831 ?
Unique (%)2.0%

Sample

1st rowaarae-alexander
2nd rowaaron-johnson
3rd rowaaron-johnson
4th rowaaron-johnson
5th rowaaron-johnson
ValueCountFrequency (%)
adam-wardzinski 349
 
0.8%
gianni-grippo 335
 
0.8%
erberth-santos 309
 
0.8%
fellipe-andrew 304
 
0.7%
thiago-macedo 266
 
0.6%
felipe-cesar 260
 
0.6%
renato-cardoso 258
 
0.6%
jaime-canuto 252
 
0.6%
joao-miyao 244
 
0.6%
jackson-sousa 242
 
0.6%
Other values (1442) 38318
93.1%
2024-07-28T10:11:19.966977image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 71473
12.9%
e 46949
 
8.5%
i 45513
 
8.2%
r 44950
 
8.1%
o 43496
 
7.9%
- 41287
 
7.5%
n 34198
 
6.2%
s 29862
 
5.4%
l 27897
 
5.0%
d 18997
 
3.4%
Other values (20) 149053
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 512252
92.5%
Dash Punctuation 41287
 
7.5%
Space Separator 135
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 71473
14.0%
e 46949
 
9.2%
i 45513
 
8.9%
r 44950
 
8.8%
o 43496
 
8.5%
n 34198
 
6.7%
s 29862
 
5.8%
l 27897
 
5.4%
d 18997
 
3.7%
c 17787
 
3.5%
Other values (17) 131130
25.6%
Dash Punctuation
ValueCountFrequency (%)
- 41287
100.0%
Space Separator
ValueCountFrequency (%)
135
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 512252
92.5%
Common 41423
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 71473
14.0%
e 46949
 
9.2%
i 45513
 
8.9%
r 44950
 
8.8%
o 43496
 
8.5%
n 34198
 
6.7%
s 29862
 
5.8%
l 27897
 
5.4%
d 18997
 
3.7%
c 17787
 
3.5%
Other values (17) 131130
25.6%
Common
ValueCountFrequency (%)
- 41287
99.7%
135
 
0.3%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 553673
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 71473
12.9%
e 46949
 
8.5%
i 45513
 
8.2%
r 44950
 
8.1%
o 43496
 
7.9%
- 41287
 
7.5%
n 34198
 
6.2%
s 29862
 
5.4%
l 27897
 
5.0%
d 18997
 
3.4%
Other values (19) 149051
26.9%
None
ValueCountFrequency (%)
ã 2
100.0%

Name
Text

MISSING 

Distinct534
Distinct (%)1.3%
Missing850
Missing (%)2.1%
Memory size2.4 MiB
2024-07-28T10:11:20.205959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length24
Median length20
Mean length13.481969
Min length8

Characters and Unicode

Total characters541328
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowaaron-johnson
2nd rowaaron-johnson
3rd rowaaron-johnson
4th rowaaron-johnson
5th rowaaron-johnson
ValueCountFrequency (%)
adam-wardzinski 349
 
0.9%
gianni-grippo 335
 
0.8%
erberth-santos 309
 
0.8%
fellipe-andrew 304
 
0.8%
thiago-macedo 266
 
0.7%
felipe-cesar 260
 
0.6%
renato-cardoso 258
 
0.6%
jaime-canuto 252
 
0.6%
joao-miyao 244
 
0.6%
jackson-sousa 242
 
0.6%
Other values (524) 37333
93.0%
2024-07-28T10:11:20.591312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 69900
12.9%
e 45907
 
8.5%
i 44500
 
8.2%
r 43884
 
8.1%
o 42433
 
7.8%
- 40433
 
7.5%
n 33459
 
6.2%
s 29238
 
5.4%
l 27213
 
5.0%
d 18583
 
3.4%
Other values (17) 145778
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 500895
92.5%
Dash Punctuation 40433
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 69900
14.0%
e 45907
 
9.2%
i 44500
 
8.9%
r 43884
 
8.8%
o 42433
 
8.5%
n 33459
 
6.7%
s 29238
 
5.8%
l 27213
 
5.4%
d 18583
 
3.7%
c 17366
 
3.5%
Other values (16) 128412
25.6%
Dash Punctuation
ValueCountFrequency (%)
- 40433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 500895
92.5%
Common 40433
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 69900
14.0%
e 45907
 
9.2%
i 44500
 
8.9%
r 43884
 
8.8%
o 42433
 
8.5%
n 33459
 
6.7%
s 29238
 
5.8%
l 27213
 
5.4%
d 18583
 
3.7%
c 17366
 
3.5%
Other values (16) 128412
25.6%
Common
ValueCountFrequency (%)
- 40433
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 541328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 69900
12.9%
e 45907
 
8.5%
i 44500
 
8.2%
r 43884
 
8.1%
o 42433
 
7.8%
- 40433
 
7.5%
n 33459
 
6.2%
s 29238
 
5.4%
l 27213
 
5.0%
d 18583
 
3.4%
Other values (17) 145778
26.9%

Opponent
Text

MISSING 

Distinct9610
Distinct (%)24.0%
Missing960
Missing (%)2.3%
Memory size2.7 MiB
2024-07-28T10:11:20.851114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length48
Median length32
Mean length19.856251
Min length3

Characters and Unicode

Total characters795084
Distinct characters77
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5701 ?
Unique (%)14.2%

Sample

1st rowQuentin Rosensweig
2nd rowNeiman Gracie
3rd rowRichie MartinezRichie Martinez
4th rowLeo NogueiraLeo Nogueira
5th rowRomulo AzevedoRomulo Azevedo
ValueCountFrequency (%)
lucas 888
 
0.9%
gabriel 816
 
0.8%
pedro 606
 
0.6%
oliveira 601
 
0.6%
silva 535
 
0.5%
sousa 500
 
0.5%
matheus 498
 
0.5%
felipe 489
 
0.5%
rafael 478
 
0.5%
santos 468
 
0.5%
Other values (8414) 94429
94.1%
2024-07-28T10:11:21.246908image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 89902
 
11.3%
e 64340
 
8.1%
o 63771
 
8.0%
60266
 
7.6%
i 59477
 
7.5%
r 53243
 
6.7%
n 46183
 
5.8%
s 34768
 
4.4%
l 33734
 
4.2%
u 23758
 
3.0%
Other values (67) 265642
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 611608
76.9%
Uppercase Letter 121656
 
15.3%
Space Separator 60266
 
7.6%
Other Punctuation 1397
 
0.2%
Dash Punctuation 155
 
< 0.1%
Final Punctuation 1
 
< 0.1%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 89902
14.7%
e 64340
10.5%
o 63771
10.4%
i 59477
9.7%
r 53243
8.7%
n 46183
 
7.6%
s 34768
 
5.7%
l 33734
 
5.5%
u 23758
 
3.9%
t 22004
 
3.6%
Other values (31) 120428
19.7%
Uppercase Letter
ValueCountFrequency (%)
M 13052
 
10.7%
A 10013
 
8.2%
R 9070
 
7.5%
S 8302
 
6.8%
C 8084
 
6.6%
L 8025
 
6.6%
J 7557
 
6.2%
G 7543
 
6.2%
B 6224
 
5.1%
D 5394
 
4.4%
Other values (19) 38392
31.6%
Other Punctuation
ValueCountFrequency (%)
. 1389
99.4%
' 6
 
0.4%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
60266
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 733264
92.2%
Common 61820
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 89902
 
12.3%
e 64340
 
8.8%
o 63771
 
8.7%
i 59477
 
8.1%
r 53243
 
7.3%
n 46183
 
6.3%
s 34768
 
4.7%
l 33734
 
4.6%
u 23758
 
3.2%
t 22004
 
3.0%
Other values (60) 242084
33.0%
Common
ValueCountFrequency (%)
60266
97.5%
. 1389
 
2.2%
- 155
 
0.3%
' 6
 
< 0.1%
, 2
 
< 0.1%
” 1
 
< 0.1%
“ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 794940
> 99.9%
None 142
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 89902
 
11.3%
e 64340
 
8.1%
o 63771
 
8.0%
60266
 
7.6%
i 59477
 
7.5%
r 53243
 
6.7%
n 46183
 
5.8%
s 34768
 
4.4%
l 33734
 
4.2%
u 23758
 
3.0%
Other values (47) 265498
33.4%
None
ValueCountFrequency (%)
ç 20
14.1%
é 18
12.7%
ã 18
12.7%
ł 17
12.0%
á 17
12.0%
ó 13
9.2%
í 10
7.0%
ú 7
 
4.9%
ä 6
 
4.2%
ô 4
 
2.8%
Other values (8) 12
8.5%
Punctuation
ValueCountFrequency (%)
” 1
50.0%
“ 1
50.0%

W/L
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing850
Missing (%)2.1%
Memory size2.0 MiB
W
28162 
L
11642 
D
 
348

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40152
Distinct characters3
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 rowL
2nd rowL
3rd rowL
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
W 28162
68.7%
L 11642
28.4%
D 348
 
0.8%
(Missing) 850
 
2.1%

Length

2024-07-28T10:11:21.378548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-28T10:11:21.491204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
w 28162
70.1%
l 11642
29.0%
d 348
 
0.9%

Most occurring characters

ValueCountFrequency (%)
W 28162
70.1%
L 11642
29.0%
D 348
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40152
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 28162
70.1%
L 11642
29.0%
D 348
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 40152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 28162
70.1%
L 11642
29.0%
D 348
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 28162
70.1%
L 11642
29.0%
D 348
 
0.9%

Method
Text

MISSING 

Distinct478
Distinct (%)1.2%
Missing1862
Missing (%)4.5%
Memory size2.2 MiB
2024-07-28T10:11:21.751148image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length22
Median length19
Mean length9.5642054
Min length2

Characters and Unicode

Total characters374343
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)0.4%

Sample

1st rowInside heel hook
2nd rowRNC
3rd rowHeel hook
4th rowPoints
5th rowCross choke
ValueCountFrequency (%)
pts 14962
20.7%
choke 4110
 
5.7%
adv 3505
 
4.8%
referee 3341
 
4.6%
decision 3341
 
4.6%
2x0 3040
 
4.2%
points 2989
 
4.1%
from 2505
 
3.5%
back 2500
 
3.5%
armbar 2433
 
3.4%
Other values (382) 29584
40.9%
2024-07-28T10:11:22.192278image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33170
 
8.9%
e 30327
 
8.1%
s 24704
 
6.6%
t 22142
 
5.9%
o 21461
 
5.7%
P 18312
 
4.9%
i 16739
 
4.5%
r 16098
 
4.3%
x 15050
 
4.0%
: 14962
 
4.0%
Other values (60) 161378
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 239962
64.1%
Uppercase Letter 50066
 
13.4%
Space Separator 33170
 
8.9%
Decimal Number 31628
 
8.4%
Other Punctuation 18468
 
4.9%
Dash Punctuation 1047
 
0.3%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30327
12.6%
s 24704
10.3%
t 22142
 
9.2%
o 21461
 
8.9%
i 16739
 
7.0%
r 16098
 
6.7%
x 15050
 
6.3%
a 12634
 
5.3%
n 12615
 
5.3%
k 9665
 
4.0%
Other values (16) 58527
24.4%
Uppercase Letter
ValueCountFrequency (%)
P 18312
36.6%
A 6268
 
12.5%
C 5072
 
10.1%
R 4941
 
9.9%
D 3804
 
7.6%
T 2135
 
4.3%
N 1602
 
3.2%
S 1431
 
2.9%
K 1280
 
2.6%
B 1125
 
2.2%
Other values (15) 4096
 
8.2%
Decimal Number
ValueCountFrequency (%)
0 11617
36.7%
2 7995
25.3%
4 3516
 
11.1%
1 2437
 
7.7%
6 1689
 
5.3%
3 1509
 
4.8%
5 1000
 
3.2%
8 788
 
2.5%
7 595
 
1.9%
9 482
 
1.5%
Other Punctuation
ValueCountFrequency (%)
: 14962
81.0%
, 3187
 
17.3%
/ 316
 
1.7%
. 2
 
< 0.1%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
33170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1047
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 290028
77.5%
Common 84315
 
22.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30327
 
10.5%
s 24704
 
8.5%
t 22142
 
7.6%
o 21461
 
7.4%
P 18312
 
6.3%
i 16739
 
5.8%
r 16098
 
5.6%
x 15050
 
5.2%
a 12634
 
4.4%
n 12615
 
4.3%
Other values (41) 99946
34.5%
Common
ValueCountFrequency (%)
33170
39.3%
: 14962
17.7%
0 11617
 
13.8%
2 7995
 
9.5%
4 3516
 
4.2%
, 3187
 
3.8%
1 2437
 
2.9%
6 1689
 
2.0%
3 1509
 
1.8%
- 1047
 
1.2%
Other values (9) 3186
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33170
 
8.9%
e 30327
 
8.1%
s 24704
 
6.6%
t 22142
 
5.9%
o 21461
 
5.7%
P 18312
 
4.9%
i 16739
 
4.5%
r 16098
 
4.3%
x 15050
 
4.0%
: 14962
 
4.0%
Other values (60) 161378
43.1%

Competition
Text

MISSING 

Distinct1775
Distinct (%)4.4%
Missing870
Missing (%)2.1%
Memory size2.3 MiB
2024-07-28T10:11:22.495462image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length19
Median length16
Mean length11.33492
Min length3

Characters and Unicode

Total characters454893
Distinct characters71
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)0.8%

Sample

1st rowKakuto 5
2nd rowNoGi Pan Ams
3rd rowKakuto Challenge
4th rowAtlanta W. Open
5th rowUAEJJF NYC Pro
ValueCountFrequency (%)
open 7224
 
8.2%
world 5231
 
6.0%
american 3731
 
4.3%
champ 3536
 
4.0%
pro 3448
 
3.9%
adcc 3267
 
3.7%
pan 3205
 
3.7%
nogi 2577
 
2.9%
slam 1976
 
2.3%
grand 1971
 
2.2%
Other values (1144) 51590
58.8%
2024-07-28T10:11:22.940324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47624
 
10.5%
a 35612
 
7.8%
r 29361
 
6.5%
o 27714
 
6.1%
n 27039
 
5.9%
i 23595
 
5.2%
e 21340
 
4.7%
l 19717
 
4.3%
C 15666
 
3.4%
p 15283
 
3.4%
Other values (61) 191942
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 266829
58.7%
Uppercase Letter 129075
28.4%
Space Separator 47624
 
10.5%
Other Punctuation 6260
 
1.4%
Decimal Number 5052
 
1.1%
Dash Punctuation 51
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35612
13.3%
r 29361
11.0%
o 27714
10.4%
n 27039
10.1%
i 23595
8.8%
e 21340
8.0%
l 19717
7.4%
p 15283
 
5.7%
m 12050
 
4.5%
s 11309
 
4.2%
Other values (19) 43809
16.4%
Uppercase Letter
ValueCountFrequency (%)
C 15666
12.1%
A 13434
10.4%
O 12307
9.5%
P 9985
 
7.7%
G 9566
 
7.4%
W 9324
 
7.2%
S 9282
 
7.2%
N 8701
 
6.7%
D 6463
 
5.0%
B 5751
 
4.5%
Other values (16) 28596
22.2%
Decimal Number
ValueCountFrequency (%)
2 1460
28.9%
1 1205
23.9%
3 485
 
9.6%
4 398
 
7.9%
6 329
 
6.5%
5 295
 
5.8%
7 241
 
4.8%
8 240
 
4.8%
0 235
 
4.7%
9 164
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 6257
> 99.9%
: 2
 
< 0.1%
/ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
47624
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 395904
87.0%
Common 58989
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35612
 
9.0%
r 29361
 
7.4%
o 27714
 
7.0%
n 27039
 
6.8%
i 23595
 
6.0%
e 21340
 
5.4%
l 19717
 
5.0%
C 15666
 
4.0%
p 15283
 
3.9%
A 13434
 
3.4%
Other values (45) 167143
42.2%
Common
ValueCountFrequency (%)
47624
80.7%
. 6257
 
10.6%
2 1460
 
2.5%
1 1205
 
2.0%
3 485
 
0.8%
4 398
 
0.7%
6 329
 
0.6%
5 295
 
0.5%
7 241
 
0.4%
8 240
 
0.4%
Other values (6) 455
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 454881
> 99.9%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47624
 
10.5%
a 35612
 
7.8%
r 29361
 
6.5%
o 27714
 
6.1%
n 27039
 
5.9%
i 23595
 
5.2%
e 21340
 
4.7%
l 19717
 
4.3%
C 15666
 
3.4%
p 15283
 
3.4%
Other values (58) 191930
42.2%
None
ValueCountFrequency (%)
ã 8
66.7%
ú 2
 
16.7%
ó 2
 
16.7%

Weight
Text

MISSING 

Distinct109
Distinct (%)0.3%
Missing1301
Missing (%)3.2%
Memory size2.0 MiB
2024-07-28T10:11:23.163122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.9092718
Min length1

Characters and Unicode

Total characters155202
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowABS
2nd row94KG
3rd rowABS
4th row94KG
5th row94KG
ValueCountFrequency (%)
abs 8421
21.2%
70kg 2970
 
7.5%
82kg 2836
 
7.1%
76kg 2670
 
6.7%
88kg 2460
 
6.2%
77kg 2325
 
5.9%
94kg 2284
 
5.8%
85kg 1551
 
3.9%
64kg 1227
 
3.1%
100kg 1095
 
2.8%
Other values (95) 11863
29.9%
2024-07-28T10:11:23.506562image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 31189
20.1%
G 31186
20.1%
7 13521
8.7%
8 10425
 
6.7%
6 8568
 
5.5%
A 8475
 
5.5%
S 8451
 
5.4%
B 8430
 
5.4%
0 8164
 
5.3%
9 7148
 
4.6%
Other values (19) 19645
12.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 89901
57.9%
Decimal Number 65287
42.1%
Lowercase Letter 10
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 31189
34.7%
G 31186
34.7%
A 8475
 
9.4%
S 8451
 
9.4%
B 8430
 
9.4%
O 1975
 
2.2%
W 54
 
0.1%
U 37
 
< 0.1%
L 36
 
< 0.1%
F 24
 
< 0.1%
Other values (4) 44
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
7 13521
20.7%
8 10425
16.0%
6 8568
13.1%
0 8164
12.5%
9 7148
10.9%
2 4234
 
6.5%
4 4211
 
6.4%
5 3999
 
6.1%
1 3998
 
6.1%
3 1019
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
g 6
60.0%
k 4
40.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89911
57.9%
Common 65291
42.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 31189
34.7%
G 31186
34.7%
A 8475
 
9.4%
S 8451
 
9.4%
B 8430
 
9.4%
O 1975
 
2.2%
W 54
 
0.1%
U 37
 
< 0.1%
L 36
 
< 0.1%
F 24
 
< 0.1%
Other values (6) 54
 
0.1%
Common
ValueCountFrequency (%)
7 13521
20.7%
8 10425
16.0%
6 8568
13.1%
0 8164
12.5%
9 7148
10.9%
2 4234
 
6.5%
4 4211
 
6.4%
5 3999
 
6.1%
1 3998
 
6.1%
3 1019
 
1.6%
Other values (3) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 31189
20.1%
G 31186
20.1%
7 13521
8.7%
8 10425
 
6.7%
6 8568
 
5.5%
A 8475
 
5.5%
S 8451
 
5.4%
B 8430
 
5.4%
0 8164
 
5.3%
9 7148
 
4.6%
Other values (19) 19645
12.7%

Stage
Categorical

MISSING 

Distinct44
Distinct (%)0.1%
Missing1048
Missing (%)2.6%
Memory size2.0 MiB
SF
10034 
4F
8718 
F
8129 
R1
4325 
SPF
2177 
Other values (39)
6571 

Length

Max length4
Median length2
Mean length1.8837663
Min length1

Characters and Unicode

Total characters75264
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowSPF
2nd rowSF
3rd rowSF
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF 10034
24.5%
4F 8718
21.3%
F 8129
19.8%
R1 4325
10.5%
SPF 2177
 
5.3%
8F 2088
 
5.1%
RR 1497
 
3.7%
R2 1178
 
2.9%
RPC 515
 
1.3%
3RD 492
 
1.2%
Other values (34) 801
 
2.0%
(Missing) 1048
 
2.6%

Length

2024-07-28T10:11:23.649698image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 10034
25.1%
4f 8718
21.8%
f 8129
20.3%
r1 4325
10.8%
spf 2177
 
5.4%
8f 2088
 
5.2%
rr 1497
 
3.7%
r2 1178
 
2.9%
rpc 515
 
1.3%
3rd 492
 
1.2%
Other values (34) 806
 
2.0%

Most occurring characters

ValueCountFrequency (%)
F 31158
41.4%
S 12345
 
16.4%
R 9969
 
13.2%
4 8786
 
11.7%
1 4388
 
5.8%
P 2948
 
3.9%
8 2107
 
2.8%
2 1184
 
1.6%
3 748
 
1.0%
D 651
 
0.9%
Other values (13) 980
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 57984
77.0%
Decimal Number 17275
 
23.0%
Space Separator 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 31158
53.7%
S 12345
 
21.3%
R 9969
 
17.2%
P 2948
 
5.1%
D 651
 
1.1%
C 565
 
1.0%
G 230
 
0.4%
L 50
 
0.1%
E 42
 
0.1%
K 22
 
< 0.1%
Other values (2) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 8786
50.9%
1 4388
25.4%
8 2107
 
12.2%
2 1184
 
6.9%
3 748
 
4.3%
5 23
 
0.1%
6 18
 
0.1%
7 11
 
0.1%
9 6
 
< 0.1%
0 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57984
77.0%
Common 17280
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 31158
53.7%
S 12345
 
21.3%
R 9969
 
17.2%
P 2948
 
5.1%
D 651
 
1.1%
C 565
 
1.0%
G 230
 
0.4%
L 50
 
0.1%
E 42
 
0.1%
K 22
 
< 0.1%
Other values (2) 4
 
< 0.1%
Common
ValueCountFrequency (%)
4 8786
50.8%
1 4388
25.4%
8 2107
 
12.2%
2 1184
 
6.9%
3 748
 
4.3%
5 23
 
0.1%
6 18
 
0.1%
7 11
 
0.1%
9 6
 
< 0.1%
5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 31158
41.4%
S 12345
 
16.4%
R 9969
 
13.2%
4 8786
 
11.7%
1 4388
 
5.8%
P 2948
 
3.9%
8 2107
 
2.8%
2 1184
 
1.6%
3 748
 
1.0%
D 651
 
0.9%
Other values (13) 980
 
1.3%

Year
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)0.1%
Missing850
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2018.838
Minimum1932
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size320.5 KiB
2024-07-28T10:11:23.787607image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1932
5-th percentile2011
Q12017
median2020
Q32022
95-th percentile2024
Maximum2024
Range92
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.7376643
Coefficient of variation (CV)0.0023467283
Kurtosis38.62521
Mean2018.838
Median Absolute Deviation (MAD)2
Skewness-3.5455842
Sum81060385
Variance22.445463
MonotonicityNot monotonic
2024-07-28T10:11:23.931526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 5504
13.4%
2023 5231
12.8%
2021 4425
10.8%
2019 4275
10.4%
2018 3765
9.2%
2017 3029
7.4%
2024 2808
6.8%
2020 2187
 
5.3%
2016 2127
 
5.2%
2015 2078
 
5.1%
Other values (42) 4723
11.5%
ValueCountFrequency (%)
1932 3
< 0.1%
1934 2
< 0.1%
1935 2
< 0.1%
1936 3
< 0.1%
1937 1
 
< 0.1%
1950 2
< 0.1%
1951 3
< 0.1%
1954 1
 
< 0.1%
1955 1
 
< 0.1%
1973 1
 
< 0.1%
ValueCountFrequency (%)
2024 2808
6.8%
2023 5231
12.8%
2022 5504
13.4%
2021 4425
10.8%
2020 2187
 
5.3%
2019 4275
10.4%
2018 3765
9.2%
2017 3029
7.4%
2016 2127
 
5.2%
2015 2078
 
5.1%

Interactions

2024-07-28T10:11:18.790916image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-28T10:11:24.018762image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
StageW/LYear
Stage1.0000.2240.094
W/L0.2241.0000.077
Year0.0940.0771.000

Missing values

2024-07-28T10:11:18.927976image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-28T10:11:19.113678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-28T10:11:19.311994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

URL TagNameOpponentW/LMethodCompetitionWeightStageYear
0aarae-alexanderNaNNaNNaNNaNNaNNaNNaNNaN
1aaron-johnsonaaron-johnsonQuentin RosensweigLInside heel hookKakuto 5ABSSPF2015.0
2aaron-johnsonaaron-johnsonNeiman GracieLRNCNoGi Pan Ams94KGSF2015.0
3aaron-johnsonaaron-johnsonRichie MartinezRichie MartinezLHeel hookKakuto ChallengeABSSF2015.0
4aaron-johnsonaaron-johnsonLeo NogueiraLeo NogueiraLPointsAtlanta W. Open94KGSF2016.0
5aaron-johnsonaaron-johnsonRomulo AzevedoRomulo AzevedoLNaNUAEJJF NYC Pro94KGSF2016.0
6aaron-johnsonaaron-johnsonAbraham MarteAbraham MarteLCross chokeUAEJJF NYC ProHWABS4F2016.0
7aaron-johnsonaaron-johnsonAndre GalvaoAndre GalvaoLChokePan AmericanABSR22016.0
8aaron-johnsonaaron-johnsonJoao SoaresLTriangleBoston Spring O.100KGF2016.0
9aaron-johnsonaaron-johnsonBernardo FariaBernardo FariaLTriangle armbarWorld Champ.ABSR22016.0
URL TagNameOpponentW/LMethodCompetitionWeightStageYear
40992vinicius-garciavinicius-garciaPedro PalharesWNaNNashville Fall OpenABSF2018.0
40993vinicius-garciavinicius-garciaStanley RosaWPointsAmerican Nats88KG4F2019.0
40994vinicius-garciavinicius-garciaJuan CleberWCanto chokeAmerican NatsABS4F2019.0
40995vinicius-garciavinicius-garciaJean CartagenaWNaNAustin SMOABS4F2019.0
40996vinicius-garciavinicius-garciaAndre ReisWNaNAustin SMOABSSF2019.0
40997vinicius-garciavinicius-garciaCody HellerWNaNAtlanta SM OpenABS4F2019.0
40998vinicius-garciavinicius-garciaDaniel OlivierWCanto chokeNew Orleans Open88KGSF2020.0
40999vinicius-garciavinicius-garciaJoshua MurdockWPointsNew Orleans OpenABSSF2020.0
41000vinicius-garciavinicius-garciaKyle RaemischWMounted X chokeF2W 15385KGSPF2020.0
41001vinicius-garciavinicius-garciaKevin VieiraWHashimoto chokePan American82KG8F2020.0

Duplicate rows

Most frequently occurring

URL TagNameOpponentW/LMethodCompetitionWeightStageYear# duplicates
0adam-wardzinskiadam-wardzinskiArya EsfandmazArya EsfandmazD---Polaris Squads 2ABSRR2020.02
1aj-agazarmaj-agazarmChoi ChoiWPts: 32x0American Nats76KG4F2013.02
2alex-munisalex-munisGabriel CostaGabriel CostaD---Fenajitsu88KGSPF2021.02
3andre-almeidaNaNNaNNaNNaNNaNNaNNaNNaN2
4arya-esfandmazarya-esfandmazAdam WardzinskiAdam WardzinskiD---Polaris Squads 2ABSRR2020.02
5brenda-larissabrenda-larissaAlexa YanesWPts: 1x0Grand Slam MSK55KGRR2021.02
6bruno-fernandesNaNNaNNaNNaNNaNNaNNaNNaN2
7claudio-calasansclaudio-calasansLucas GualbertoLucas GualbertoLPts: 2x0Fenajitsu82KGSPF2021.02
8claudio-calasansclaudio-calasansSonoda KendyWArmbarAsian Open88KGSF2014.02
9eldar-rafigaeveldar-rafigaevMax ArnoldWFootlockCroatia ProABSRR2018.02