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Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations94
Missing cells68
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.6 KiB
Average record size in memory757.7 B

Variable types

Text1
Categorical10

Alerts

SITE_SUBTYPE_3 has constant value "NS" Constant
PRIMARY_HISTOLOGY has constant value "gastrointestinal_stromal_tumour" Constant
HISTOLOGY_SUBTYPE_3 has constant value "NS" Constant
EFO is highly overall correlated with HISTOLOGY_SUBTYPE_1 and 2 other fieldsHigh correlation
HISTOLOGY_SUBTYPE_1 is highly overall correlated with EFO and 1 other fieldsHigh correlation
HISTOLOGY_SUBTYPE_2 is highly overall correlated with EFOHigh correlation
NCI_CODE is highly overall correlated with EFO and 3 other fieldsHigh correlation
PRIMARY_SITE is highly overall correlated with NCI_CODEHigh correlation
SITE_SUBTYPE_1 is highly overall correlated with NCI_CODEHigh correlation
PRIMARY_SITE is highly imbalanced (91.5%) Imbalance
SITE_SUBTYPE_1 is highly imbalanced (91.5%) Imbalance
HISTOLOGY_SUBTYPE_2 is highly imbalanced (71.8%) Imbalance
EFO is highly imbalanced (60.9%) Imbalance
EFO has 68 (72.3%) missing values Missing
COSMIC_PHENOTYPE_ID has unique values Unique

Reproduction

Analysis started2025-07-15 00:43:24.358706
Analysis finished2025-07-15 00:43:25.005719
Duration0.65 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

COSMIC_PHENOTYPE_ID
Text

Unique 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2025-07-15T00:43:25.169937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.202128
Min length12

Characters and Unicode

Total characters1147
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

Unique94 ?
Unique (%)100.0%

Sample

1st rowCOSO318721653
2nd rowCOSO97595385
3rd rowCOSO287721665
4th rowCOSO36605385
5th rowCOSO60695381
ValueCountFrequency (%)
coso318721653 1
 
1.1%
coso97595385 1
 
1.1%
coso287721665 1
 
1.1%
coso36605385 1
 
1.1%
coso60695381 1
 
1.1%
coso34435546 1
 
1.1%
coso35205546 1
 
1.1%
coso31875763 1
 
1.1%
coso28815381 1
 
1.1%
coso36845546 1
 
1.1%
Other values (84) 84
89.4%
2025-07-15T00:43:25.452266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 188
16.4%
5 147
12.8%
3 141
12.3%
8 96
8.4%
C 94
8.2%
S 94
8.2%
6 93
8.1%
7 89
7.8%
1 76
6.6%
4 49
 
4.3%
Other values (3) 80
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 188
16.4%
5 147
12.8%
3 141
12.3%
8 96
8.4%
C 94
8.2%
S 94
8.2%
6 93
8.1%
7 89
7.8%
1 76
6.6%
4 49
 
4.3%
Other values (3) 80
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 188
16.4%
5 147
12.8%
3 141
12.3%
8 96
8.4%
C 94
8.2%
S 94
8.2%
6 93
8.1%
7 89
7.8%
1 76
6.6%
4 49
 
4.3%
Other values (3) 80
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 188
16.4%
5 147
12.8%
3 141
12.3%
8 96
8.4%
C 94
8.2%
S 94
8.2%
6 93
8.1%
7 89
7.8%
1 76
6.6%
4 49
 
4.3%
Other values (3) 80
7.0%

PRIMARY_SITE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
soft_tissue
93 
large_intestine
 
1

Length

Max length15
Median length11
Mean length11.042553
Min length11

Characters and Unicode

Total characters1038
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

Unique1 ?
Unique (%)1.1%

Sample

1st rowsoft_tissue
2nd rowsoft_tissue
3rd rowsoft_tissue
4th rowsoft_tissue
5th rowsoft_tissue

Common Values

ValueCountFrequency (%)
soft_tissue 93
98.9%
large_intestine 1
 
1.1%

Length

2025-07-15T00:43:25.549344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:25.681216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
soft_tissue 93
98.9%
large_intestine 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
s 280
27.0%
t 188
18.1%
e 96
 
9.2%
i 95
 
9.2%
_ 94
 
9.1%
o 93
 
9.0%
f 93
 
9.0%
u 93
 
9.0%
n 2
 
0.2%
l 1
 
0.1%
Other values (3) 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 280
27.0%
t 188
18.1%
e 96
 
9.2%
i 95
 
9.2%
_ 94
 
9.1%
o 93
 
9.0%
f 93
 
9.0%
u 93
 
9.0%
n 2
 
0.2%
l 1
 
0.1%
Other values (3) 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 280
27.0%
t 188
18.1%
e 96
 
9.2%
i 95
 
9.2%
_ 94
 
9.1%
o 93
 
9.0%
f 93
 
9.0%
u 93
 
9.0%
n 2
 
0.2%
l 1
 
0.1%
Other values (3) 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 280
27.0%
t 188
18.1%
e 96
 
9.2%
i 95
 
9.2%
_ 94
 
9.1%
o 93
 
9.0%
f 93
 
9.0%
u 93
 
9.0%
n 2
 
0.2%
l 1
 
0.1%
Other values (3) 3
 
0.3%

SITE_SUBTYPE_1
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
fibrous_tissue_and_uncertain_origin
93 
rectum
 
1

Length

Max length35
Median length35
Mean length34.691489
Min length6

Characters and Unicode

Total characters3261
Distinct characters16
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

Unique1 ?
Unique (%)1.1%

Sample

1st rowfibrous_tissue_and_uncertain_origin
2nd rowfibrous_tissue_and_uncertain_origin
3rd rowfibrous_tissue_and_uncertain_origin
4th rowfibrous_tissue_and_uncertain_origin
5th rowfibrous_tissue_and_uncertain_origin

Common Values

ValueCountFrequency (%)
fibrous_tissue_and_uncertain_origin 93
98.9%
rectum 1
 
1.1%

Length

2025-07-15T00:43:25.742196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:25.790254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fibrous_tissue_and_uncertain_origin 93
98.9%
rectum 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
i 465
14.3%
_ 372
11.4%
n 372
11.4%
u 280
8.6%
r 280
8.6%
s 279
8.6%
t 187
5.7%
e 187
5.7%
o 186
 
5.7%
a 186
 
5.7%
Other values (6) 467
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3261
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 465
14.3%
_ 372
11.4%
n 372
11.4%
u 280
8.6%
r 280
8.6%
s 279
8.6%
t 187
5.7%
e 187
5.7%
o 186
 
5.7%
a 186
 
5.7%
Other values (6) 467
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3261
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 465
14.3%
_ 372
11.4%
n 372
11.4%
u 280
8.6%
r 280
8.6%
s 279
8.6%
t 187
5.7%
e 187
5.7%
o 186
 
5.7%
a 186
 
5.7%
Other values (6) 467
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3261
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 465
14.3%
_ 372
11.4%
n 372
11.4%
u 280
8.6%
r 280
8.6%
s 279
8.6%
t 187
5.7%
e 187
5.7%
o 186
 
5.7%
a 186
 
5.7%
Other values (6) 467
14.3%

SITE_SUBTYPE_2
Categorical

Distinct28
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
small_intestine
10 
stomach
9 
large_intestine
8 
retroperitoneum
6 
peritoneum
 
5
Other values (23)
56 

Length

Max length43
Median length20
Mean length12.702128
Min length2

Characters and Unicode

Total characters1194
Distinct characters29
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

Unique9 ?
Unique (%)9.6%

Sample

1st rowretroperitoneum
2nd rowextra-gastrointestinal_site
3rd rowlarge_intestine
4th rowgastrointestinal_tract_(site_indeterminate)
5th rowmesocolon

Common Values

ValueCountFrequency (%)
small_intestine 10
 
10.6%
stomach 9
 
9.6%
large_intestine 8
 
8.5%
retroperitoneum 6
 
6.4%
peritoneum 5
 
5.3%
gastrointestinal_tract_(site_indeterminate) 5
 
5.3%
NS 5
 
5.3%
extra-gastrointestinal_site 4
 
4.3%
oesophagus 4
 
4.3%
omentum 4
 
4.3%
Other values (18) 34
36.2%

Length

2025-07-15T00:43:25.860715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
small_intestine 10
 
10.6%
stomach 9
 
9.6%
large_intestine 8
 
8.5%
retroperitoneum 6
 
6.4%
peritoneum 5
 
5.3%
gastrointestinal_tract_(site_indeterminate 5
 
5.3%
ns 5
 
5.3%
extra-gastrointestinal_site 4
 
4.3%
oesophagus 4
 
4.3%
omentum 4
 
4.3%
Other values (18) 34
36.2%

Most occurring characters

ValueCountFrequency (%)
e 153
12.8%
t 147
12.3%
i 109
9.1%
a 101
8.5%
n 97
 
8.1%
s 88
 
7.4%
r 71
 
5.9%
o 63
 
5.3%
m 59
 
4.9%
l 59
 
4.9%
Other values (19) 247
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 153
12.8%
t 147
12.3%
i 109
9.1%
a 101
8.5%
n 97
 
8.1%
s 88
 
7.4%
r 71
 
5.9%
o 63
 
5.3%
m 59
 
4.9%
l 59
 
4.9%
Other values (19) 247
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 153
12.8%
t 147
12.3%
i 109
9.1%
a 101
8.5%
n 97
 
8.1%
s 88
 
7.4%
r 71
 
5.9%
o 63
 
5.3%
m 59
 
4.9%
l 59
 
4.9%
Other values (19) 247
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 153
12.8%
t 147
12.3%
i 109
9.1%
a 101
8.5%
n 97
 
8.1%
s 88
 
7.4%
r 71
 
5.9%
o 63
 
5.3%
m 59
 
4.9%
l 59
 
4.9%
Other values (19) 247
20.7%

SITE_SUBTYPE_3
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
NS
94 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters188
Distinct characters2
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 rowNS
2nd rowNS
3rd rowNS
4th rowNS
5th rowNS

Common Values

ValueCountFrequency (%)
NS 94
100.0%

Length

2025-07-15T00:43:25.936549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:25.977729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ns 94
100.0%

Most occurring characters

ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

PRIMARY_HISTOLOGY
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
gastrointestinal_stromal_tumour
94 

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters2914
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 rowgastrointestinal_stromal_tumour
2nd rowgastrointestinal_stromal_tumour
3rd rowgastrointestinal_stromal_tumour
4th rowgastrointestinal_stromal_tumour
5th rowgastrointestinal_stromal_tumour

Common Values

ValueCountFrequency (%)
gastrointestinal_stromal_tumour 94
100.0%

Length

2025-07-15T00:43:26.028015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:26.070738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gastrointestinal_stromal_tumour 94
100.0%

Most occurring characters

ValueCountFrequency (%)
t 470
16.1%
s 282
9.7%
a 282
9.7%
r 282
9.7%
o 282
9.7%
m 188
 
6.5%
i 188
 
6.5%
n 188
 
6.5%
l 188
 
6.5%
u 188
 
6.5%
Other values (3) 376
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 470
16.1%
s 282
9.7%
a 282
9.7%
r 282
9.7%
o 282
9.7%
m 188
 
6.5%
i 188
 
6.5%
n 188
 
6.5%
l 188
 
6.5%
u 188
 
6.5%
Other values (3) 376
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 470
16.1%
s 282
9.7%
a 282
9.7%
r 282
9.7%
o 282
9.7%
m 188
 
6.5%
i 188
 
6.5%
n 188
 
6.5%
l 188
 
6.5%
u 188
 
6.5%
Other values (3) 376
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 470
16.1%
s 282
9.7%
a 282
9.7%
r 282
9.7%
o 282
9.7%
m 188
 
6.5%
i 188
 
6.5%
n 188
 
6.5%
l 188
 
6.5%
u 188
 
6.5%
Other values (3) 376
12.9%

HISTOLOGY_SUBTYPE_1
Categorical

High correlation 

Distinct9
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
NS
24 
spindle
21 
spindle_and_epithelioid
17 
epithelioid
14 
dedifferentiated
8 
Other values (4)
10 

Length

Max length46
Median length23
Mean length11.968085
Min length2

Characters and Unicode

Total characters1125
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

Unique2 ?
Unique (%)2.1%

Sample

1st rowdedifferentiated
2nd rowspindle
3rd rowdedifferentiated
4th rowspindle
5th rowNS

Common Values

ValueCountFrequency (%)
NS 24
25.5%
spindle 21
22.3%
spindle_and_epithelioid 17
18.1%
epithelioid 14
14.9%
dedifferentiated 8
 
8.5%
transdifferentiated 6
 
6.4%
diffuse_interstitial_cell_of Cajal_hyperplasia 2
 
2.1%
spindle_and_epithelial_and_rhabdoid 1
 
1.1%
unusual_sub-type 1
 
1.1%

Length

2025-07-15T00:43:26.277744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:26.353129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ns 24
25.0%
spindle 21
21.9%
spindle_and_epithelioid 17
17.7%
epithelioid 14
14.6%
dedifferentiated 8
 
8.3%
transdifferentiated 6
 
6.2%
diffuse_interstitial_cell_of 2
 
2.1%
cajal_hyperplasia 2
 
2.1%
spindle_and_epithelial_and_rhabdoid 1
 
1.0%
unusual_sub-type 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 173
15.4%
e 162
14.4%
d 129
11.5%
l 83
7.4%
n 81
7.2%
p 76
 
6.8%
t 73
 
6.5%
s 53
 
4.7%
a 52
 
4.6%
_ 47
 
4.2%
Other values (14) 196
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 173
15.4%
e 162
14.4%
d 129
11.5%
l 83
7.4%
n 81
7.2%
p 76
 
6.8%
t 73
 
6.5%
s 53
 
4.7%
a 52
 
4.6%
_ 47
 
4.2%
Other values (14) 196
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 173
15.4%
e 162
14.4%
d 129
11.5%
l 83
7.4%
n 81
7.2%
p 76
 
6.8%
t 73
 
6.5%
s 53
 
4.7%
a 52
 
4.6%
_ 47
 
4.2%
Other values (14) 196
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 173
15.4%
e 162
14.4%
d 129
11.5%
l 83
7.4%
n 81
7.2%
p 76
 
6.8%
t 73
 
6.5%
s 53
 
4.7%
a 52
 
4.6%
_ 47
 
4.2%
Other values (14) 196
17.4%

HISTOLOGY_SUBTYPE_2
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
NS
82 
anaplastic_and_spindle
 
3
rhabdomyoblastic_and_spindle
 
2
anaplastic_and_epithelioid
 
1
rhabdomyoblastic_and_epithelioid
 
1
Other values (5)
 
5

Length

Max length51
Median length2
Mean length5.5
Min length2

Characters and Unicode

Total characters517
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

Unique7 ?
Unique (%)7.4%

Sample

1st rowNS
2nd rowNS
3rd rowanaplastic_and_epithelioid
4th rowNS
5th rowNS

Common Values

ValueCountFrequency (%)
NS 82
87.2%
anaplastic_and_spindle 3
 
3.2%
rhabdomyoblastic_and_spindle 2
 
2.1%
anaplastic_and_epithelioid 1
 
1.1%
rhabdomyoblastic_and_epithelioid 1
 
1.1%
plexiform 1
 
1.1%
anaplastic_and_spindle_and_epithelioid 1
 
1.1%
rhabdomyoblastic_and_anaplastic 1
 
1.1%
rhabdomyoblastic_and_epithelioid_and_spindle 1
 
1.1%
rhabdomyoblastic_and_chondrosarcomatous_and_spindle 1
 
1.1%

Length

2025-07-15T00:43:26.466285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:26.543017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ns 82
87.2%
anaplastic_and_spindle 3
 
3.2%
rhabdomyoblastic_and_spindle 2
 
2.1%
anaplastic_and_epithelioid 1
 
1.1%
rhabdomyoblastic_and_epithelioid 1
 
1.1%
plexiform 1
 
1.1%
anaplastic_and_spindle_and_epithelioid 1
 
1.1%
rhabdomyoblastic_and_anaplastic 1
 
1.1%
rhabdomyoblastic_and_epithelioid_and_spindle 1
 
1.1%
rhabdomyoblastic_and_chondrosarcomatous_and_spindle 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
N 82
15.9%
S 82
15.9%
a 46
 
8.9%
i 33
 
6.4%
d 33
 
6.4%
n 29
 
5.6%
_ 28
 
5.4%
l 25
 
4.8%
s 22
 
4.3%
o 21
 
4.1%
Other values (12) 116
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 82
15.9%
S 82
15.9%
a 46
 
8.9%
i 33
 
6.4%
d 33
 
6.4%
n 29
 
5.6%
_ 28
 
5.4%
l 25
 
4.8%
s 22
 
4.3%
o 21
 
4.1%
Other values (12) 116
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 82
15.9%
S 82
15.9%
a 46
 
8.9%
i 33
 
6.4%
d 33
 
6.4%
n 29
 
5.6%
_ 28
 
5.4%
l 25
 
4.8%
s 22
 
4.3%
o 21
 
4.1%
Other values (12) 116
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 82
15.9%
S 82
15.9%
a 46
 
8.9%
i 33
 
6.4%
d 33
 
6.4%
n 29
 
5.6%
_ 28
 
5.4%
l 25
 
4.8%
s 22
 
4.3%
o 21
 
4.1%
Other values (12) 116
22.4%

HISTOLOGY_SUBTYPE_3
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
NS
94 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters188
Distinct characters2
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 rowNS
2nd rowNS
3rd rowNS
4th rowNS
5th rowNS

Common Values

ValueCountFrequency (%)
NS 94
100.0%

Length

2025-07-15T00:43:26.651259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:26.692260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ns 94
100.0%

Most occurring characters

ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 94
50.0%
S 94
50.0%

NCI_CODE
Categorical

High correlation 

Distinct7
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
C3868
24 
C27792
20 
C27793
17 
C179932
14 
C3486
14 
Other values (2)
5 

Length

Max length7
Median length6
Mean length5.712766
Min length5

Characters and Unicode

Total characters537
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 rowC179932
2nd rowC27792
3rd rowC179932
4th rowC27792
5th rowC3868

Common Values

ValueCountFrequency (%)
C3868 24
25.5%
C27792 20
21.3%
C27793 17
18.1%
C179932 14
14.9%
C3486 14
14.9%
C5811 3
 
3.2%
C27735 2
 
2.1%

Length

2025-07-15T00:43:26.751108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:26.821596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
c3868 24
25.5%
c27792 20
21.3%
c27793 17
18.1%
c179932 14
14.9%
c3486 14
14.9%
c5811 3
 
3.2%
c27735 2
 
2.1%

Most occurring characters

ValueCountFrequency (%)
C 94
17.5%
7 92
17.1%
2 73
13.6%
3 71
13.2%
8 65
12.1%
9 65
12.1%
6 38
7.1%
1 20
 
3.7%
4 14
 
2.6%
5 5
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 94
17.5%
7 92
17.1%
2 73
13.6%
3 71
13.2%
8 65
12.1%
9 65
12.1%
6 38
7.1%
1 20
 
3.7%
4 14
 
2.6%
5 5
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 94
17.5%
7 92
17.1%
2 73
13.6%
3 71
13.2%
8 65
12.1%
9 65
12.1%
6 38
7.1%
1 20
 
3.7%
4 14
 
2.6%
5 5
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 94
17.5%
7 92
17.1%
2 73
13.6%
3 71
13.2%
8 65
12.1%
9 65
12.1%
6 38
7.1%
1 20
 
3.7%
4 14
 
2.6%
5 5
 
0.9%

EFO
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)7.7%
Missing68
Missing (%)72.3%
Memory size7.5 KiB
http://purl.obolibrary.org/obo/MONDO_0011719
24 
http://www.ebi.ac.uk/efo/EFO_1000192
 
2

Length

Max length44
Median length44
Mean length43.384615
Min length36

Characters and Unicode

Total characters1128
Distinct characters32
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 rowhttp://purl.obolibrary.org/obo/MONDO_0011719
2nd rowhttp://purl.obolibrary.org/obo/MONDO_0011719
3rd rowhttp://purl.obolibrary.org/obo/MONDO_0011719
4th rowhttp://purl.obolibrary.org/obo/MONDO_0011719
5th rowhttp://purl.obolibrary.org/obo/MONDO_0011719

Common Values

ValueCountFrequency (%)
http://purl.obolibrary.org/obo/MONDO_0011719 24
 
25.5%
http://www.ebi.ac.uk/efo/EFO_1000192 2
 
2.1%
(Missing) 68
72.3%

Length

2025-07-15T00:43:26.917789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T00:43:26.970190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
http://purl.obolibrary.org/obo/mondo_0011719 24
92.3%
http://www.ebi.ac.uk/efo/efo_1000192 2
 
7.7%

Most occurring characters

ValueCountFrequency (%)
o 122
 
10.8%
/ 104
 
9.2%
r 96
 
8.5%
1 76
 
6.7%
b 74
 
6.6%
. 54
 
4.8%
0 54
 
4.8%
t 52
 
4.6%
p 50
 
4.4%
O 50
 
4.4%
Other values (22) 396
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 122
 
10.8%
/ 104
 
9.2%
r 96
 
8.5%
1 76
 
6.7%
b 74
 
6.6%
. 54
 
4.8%
0 54
 
4.8%
t 52
 
4.6%
p 50
 
4.4%
O 50
 
4.4%
Other values (22) 396
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 122
 
10.8%
/ 104
 
9.2%
r 96
 
8.5%
1 76
 
6.7%
b 74
 
6.6%
. 54
 
4.8%
0 54
 
4.8%
t 52
 
4.6%
p 50
 
4.4%
O 50
 
4.4%
Other values (22) 396
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 122
 
10.8%
/ 104
 
9.2%
r 96
 
8.5%
1 76
 
6.7%
b 74
 
6.6%
. 54
 
4.8%
0 54
 
4.8%
t 52
 
4.6%
p 50
 
4.4%
O 50
 
4.4%
Other values (22) 396
35.1%

Correlations

2025-07-15T00:43:27.019451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EFOHISTOLOGY_SUBTYPE_1HISTOLOGY_SUBTYPE_2NCI_CODEPRIMARY_SITESITE_SUBTYPE_1SITE_SUBTYPE_2
EFO1.0000.9791.0000.7160.2520.2520.000
HISTOLOGY_SUBTYPE_10.9791.0000.3460.9000.0000.0000.000
HISTOLOGY_SUBTYPE_21.0000.3461.0000.2000.0000.0000.000
NCI_CODE0.7160.9000.2001.0000.6590.6590.000
PRIMARY_SITE0.2520.0000.0000.6591.0000.4860.000
SITE_SUBTYPE_10.2520.0000.0000.6590.4861.0000.000
SITE_SUBTYPE_20.0000.0000.0000.0000.0000.0001.000

Missing values

2025-07-15T00:43:24.857159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-15T00:43:24.951561image/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.

Sample

COSMIC_PHENOTYPE_IDPRIMARY_SITESITE_SUBTYPE_1SITE_SUBTYPE_2SITE_SUBTYPE_3PRIMARY_HISTOLOGYHISTOLOGY_SUBTYPE_1HISTOLOGY_SUBTYPE_2HISTOLOGY_SUBTYPE_3NCI_CODEEFO
170COSO318721653soft_tissuefibrous_tissue_and_uncertain_originretroperitoneumNSgastrointestinal_stromal_tumourdedifferentiatedNSNSC179932NaN
317COSO97595385soft_tissuefibrous_tissue_and_uncertain_originextra-gastrointestinal_siteNSgastrointestinal_stromal_tumourspindleNSNSC27792NaN
322COSO287721665soft_tissuefibrous_tissue_and_uncertain_originlarge_intestineNSgastrointestinal_stromal_tumourdedifferentiatedanaplastic_and_epithelioidNSC179932NaN
365COSO36605385soft_tissuefibrous_tissue_and_uncertain_origingastrointestinal_tract_(site_indeterminate)NSgastrointestinal_stromal_tumourspindleNSNSC27792NaN
394COSO60695381soft_tissuefibrous_tissue_and_uncertain_originmesocolonNSgastrointestinal_stromal_tumourNSNSNSC3868http://purl.obolibrary.org/obo/MONDO_0011719
640COSO34435546soft_tissuefibrous_tissue_and_uncertain_originmediastinumNSgastrointestinal_stromal_tumourepithelioidNSNSC3486NaN
709COSO35205546soft_tissuefibrous_tissue_and_uncertain_originmesenteryNSgastrointestinal_stromal_tumourepithelioidNSNSC3486NaN
855COSO31875763soft_tissuefibrous_tissue_and_uncertain_originretroperitoneumNSgastrointestinal_stromal_tumourspindle_and_epithelioidNSNSC27793NaN
911COSO28815381soft_tissuefibrous_tissue_and_uncertain_originsmall_intestineNSgastrointestinal_stromal_tumourNSNSNSC3868http://purl.obolibrary.org/obo/MONDO_0011719
932COSO36845546soft_tissuefibrous_tissue_and_uncertain_originpelvic_cavityNSgastrointestinal_stromal_tumourepithelioidNSNSC3486NaN
COSMIC_PHENOTYPE_IDPRIMARY_SITESITE_SUBTYPE_1SITE_SUBTYPE_2SITE_SUBTYPE_3PRIMARY_HISTOLOGYHISTOLOGY_SUBTYPE_1HISTOLOGY_SUBTYPE_2HISTOLOGY_SUBTYPE_3NCI_CODEEFO
6680COSO36075546soft_tissuefibrous_tissue_and_uncertain_originstomachNSgastrointestinal_stromal_tumourepithelioidNSNSC3486NaN
6690COSO36605381soft_tissuefibrous_tissue_and_uncertain_origingastrointestinal_tract_(site_indeterminate)NSgastrointestinal_stromal_tumourNSNSNSC3868http://purl.obolibrary.org/obo/MONDO_0011719
6736COSO57285385soft_tissuefibrous_tissue_and_uncertain_originhipNSgastrointestinal_stromal_tumourspindleNSNSC27792NaN
6778COSO318721673soft_tissuefibrous_tissue_and_uncertain_originretroperitoneumNSgastrointestinal_stromal_tumourtransdifferentiatedrhabdomyoblastic_and_chondrosarcomatous_and_spindleNSC179932NaN
6822COSO37735546soft_tissuefibrous_tissue_and_uncertain_originabdomenNSgastrointestinal_stromal_tumourepithelioidNSNSC3486NaN
6839COSO37565381soft_tissuefibrous_tissue_and_uncertain_originliverNSgastrointestinal_stromal_tumourNSNSNSC3868http://purl.obolibrary.org/obo/MONDO_0011719
6941COSO31355546soft_tissuefibrous_tissue_and_uncertain_originNSNSgastrointestinal_stromal_tumourepithelioidNSNSC3486NaN
6957COSO34395385soft_tissuefibrous_tissue_and_uncertain_originabdominal_wallNSgastrointestinal_stromal_tumourspindleNSNSC27792NaN
7052COSO37735763soft_tissuefibrous_tissue_and_uncertain_originabdomenNSgastrointestinal_stromal_tumourspindle_and_epithelioidNSNSC27793NaN
7124COSO287721654soft_tissuefibrous_tissue_and_uncertain_originlarge_intestineNSgastrointestinal_stromal_tumourdedifferentiatedanaplastic_and_spindleNSC179932NaN