Overview

Brought to you by YData

Dataset statistics

Number of variables60
Number of observations236488
Missing cells3467408
Missing cells (%)24.4%
Total size in memory108.3 MiB
Average record size in memory480.0 B

Variable types

Text60

Dataset

DescriptionField Museum of Natural History (Zoology) Mammal Collection 0011504-250127130748423
URLhttps://doi.org/10.15468/dl.8cjh4u

Alerts

accessRights has constant value "http://fieldmuseum.org/about/copyright-information" Constant
language has constant value "en" Constant
license has constant value "https://creativecommons.org/publicdomain/zero/1.0/" Constant
rightsHolder has constant value "The Field Museum of Natural History" Constant
type has constant value "PhysicalObject" Constant
institutionID has constant value "FMNH" Constant
collectionID has constant value "Mammals" Constant
datasetID has constant value "mammals-24-sep-2020" Constant
institutionCode has constant value "FMNH" Constant
collectionCode has constant value "Mammals" Constant
datasetName has constant value "Philippines Natural History" Constant
ownerInstitutionCode has constant value "FMNH" Constant
basisOfRecord has constant value "PreservedSpecimen" Constant
associatedSequences has constant value "GU731517" Constant
verbatimElevation has constant value "-" Constant
kingdom has constant value "Animalia" Constant
phylum has constant value "Chordata" Constant
class has constant value "Mammalia" Constant
datasetName has 236407 (> 99.9%) missing values Missing
recordNumber has 15120 (6.4%) missing values Missing
recordedBy has 2691 (1.1%) missing values Missing
sex has 16090 (6.8%) missing values Missing
associatedMedia has 235480 (99.6%) missing values Missing
associatedSequences has 236487 (> 99.9%) missing values Missing
fieldNumber has 14577 (6.2%) missing values Missing
startDayOfYear has 17050 (7.2%) missing values Missing
year has 7781 (3.3%) missing values Missing
month has 10882 (4.6%) missing values Missing
day has 17050 (7.2%) missing values Missing
continent has 4336 (1.8%) missing values Missing
waterBody has 236467 (> 99.9%) missing values Missing
islandGroup has 235141 (99.4%) missing values Missing
island has 234964 (99.4%) missing values Missing
stateProvince has 9931 (4.2%) missing values Missing
county has 94287 (39.9%) missing values Missing
locality has 9170 (3.9%) missing values Missing
minimumElevationInMeters has 116627 (49.3%) missing values Missing
maximumElevationInMeters has 233009 (98.5%) missing values Missing
verbatimElevation has 236487 (> 99.9%) missing values Missing
locationRemarks has 94113 (39.8%) missing values Missing
decimalLatitude has 32326 (13.7%) missing values Missing
decimalLongitude has 32129 (13.6%) missing values Missing
verbatimLatitude has 44305 (18.7%) missing values Missing
verbatimLongitude has 44313 (18.7%) missing values Missing
georeferenceProtocol has 68457 (28.9%) missing values Missing
typeStatus has 235877 (99.7%) missing values Missing
identifiedBy has 234549 (99.2%) missing values Missing
dateIdentified has 233049 (98.5%) missing values Missing
subgenus has 216134 (91.4%) missing values Missing
specificEpithet has 9549 (4.0%) missing values Missing
gbifID has unique values Unique
occurrenceID has unique values Unique
catalogNumber has unique values Unique
organismID has unique values Unique

Reproduction

Analysis started2025-02-14 20:18:33.035180
Analysis finished2025-02-14 20:18:38.778303
Duration5.74 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct236488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:38.929062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.192047799
Min length9

Characters and Unicode

Total characters2173809
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

Unique236488 ?
Unique (%)100.0%

Sample

1st row1427523073
2nd row665802325
3rd row665880341
4th row665877835
5th row665908970
ValueCountFrequency (%)
1427523073 1
 
< 0.1%
665757530 1
 
< 0.1%
665890136 1
 
< 0.1%
1805734887 1
 
< 0.1%
665768830 1
 
< 0.1%
665880341 1
 
< 0.1%
665877835 1
 
< 0.1%
665908970 1
 
< 0.1%
665832820 1
 
< 0.1%
665731309 1
 
< 0.1%
Other values (236478) 236478
> 99.9%
2025-02-14T15:18:39.188017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 495400
22.8%
5 344039
15.8%
8 231674
10.7%
7 218210
10.0%
1 163817
 
7.5%
2 161828
 
7.4%
9 149809
 
6.9%
4 141539
 
6.5%
0 139564
 
6.4%
3 127929
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2173809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 495400
22.8%
5 344039
15.8%
8 231674
10.7%
7 218210
10.0%
1 163817
 
7.5%
2 161828
 
7.4%
9 149809
 
6.9%
4 141539
 
6.5%
0 139564
 
6.4%
3 127929
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2173809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 495400
22.8%
5 344039
15.8%
8 231674
10.7%
7 218210
10.0%
1 163817
 
7.5%
2 161828
 
7.4%
9 149809
 
6.9%
4 141539
 
6.5%
0 139564
 
6.4%
3 127929
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2173809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 495400
22.8%
5 344039
15.8%
8 231674
10.7%
7 218210
10.0%
1 163817
 
7.5%
2 161828
 
7.4%
9 149809
 
6.9%
4 141539
 
6.5%
0 139564
 
6.4%
3 127929
 
5.9%

accessRights
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:39.241236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters11824400
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://fieldmuseum.org/about/copyright-information
2nd rowhttp://fieldmuseum.org/about/copyright-information
3rd rowhttp://fieldmuseum.org/about/copyright-information
4th rowhttp://fieldmuseum.org/about/copyright-information
5th rowhttp://fieldmuseum.org/about/copyright-information
ValueCountFrequency (%)
http://fieldmuseum.org/about/copyright-information 236488
100.0%
2025-02-14T15:18:39.324760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1182440
 
10.0%
t 1182440
 
10.0%
/ 945952
 
8.0%
i 945952
 
8.0%
u 709464
 
6.0%
m 709464
 
6.0%
r 709464
 
6.0%
a 472976
 
4.0%
g 472976
 
4.0%
h 472976
 
4.0%
Other values (13) 4020296
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11824400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1182440
 
10.0%
t 1182440
 
10.0%
/ 945952
 
8.0%
i 945952
 
8.0%
u 709464
 
6.0%
m 709464
 
6.0%
r 709464
 
6.0%
a 472976
 
4.0%
g 472976
 
4.0%
h 472976
 
4.0%
Other values (13) 4020296
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11824400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1182440
 
10.0%
t 1182440
 
10.0%
/ 945952
 
8.0%
i 945952
 
8.0%
u 709464
 
6.0%
m 709464
 
6.0%
r 709464
 
6.0%
a 472976
 
4.0%
g 472976
 
4.0%
h 472976
 
4.0%
Other values (13) 4020296
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11824400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1182440
 
10.0%
t 1182440
 
10.0%
/ 945952
 
8.0%
i 945952
 
8.0%
u 709464
 
6.0%
m 709464
 
6.0%
r 709464
 
6.0%
a 472976
 
4.0%
g 472976
 
4.0%
h 472976
 
4.0%
Other values (13) 4020296
34.0%

language
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:39.355398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters472976
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 rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 236488
100.0%
2025-02-14T15:18:39.492140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 236488
50.0%
n 236488
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 472976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 236488
50.0%
n 236488
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 472976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 236488
50.0%
n 236488
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 472976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 236488
50.0%
n 236488
50.0%

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:39.521008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters11824400
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 rowhttps://creativecommons.org/publicdomain/zero/1.0/
2nd rowhttps://creativecommons.org/publicdomain/zero/1.0/
3rd rowhttps://creativecommons.org/publicdomain/zero/1.0/
4th rowhttps://creativecommons.org/publicdomain/zero/1.0/
5th rowhttps://creativecommons.org/publicdomain/zero/1.0/
ValueCountFrequency (%)
https://creativecommons.org/publicdomain/zero/1.0 236488
100.0%
2025-02-14T15:18:39.602432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1418928
 
12.0%
o 1182440
 
10.0%
i 709464
 
6.0%
m 709464
 
6.0%
c 709464
 
6.0%
r 709464
 
6.0%
e 709464
 
6.0%
t 709464
 
6.0%
. 472976
 
4.0%
n 472976
 
4.0%
Other values (14) 4020296
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11824400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 1418928
 
12.0%
o 1182440
 
10.0%
i 709464
 
6.0%
m 709464
 
6.0%
c 709464
 
6.0%
r 709464
 
6.0%
e 709464
 
6.0%
t 709464
 
6.0%
. 472976
 
4.0%
n 472976
 
4.0%
Other values (14) 4020296
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11824400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 1418928
 
12.0%
o 1182440
 
10.0%
i 709464
 
6.0%
m 709464
 
6.0%
c 709464
 
6.0%
r 709464
 
6.0%
e 709464
 
6.0%
t 709464
 
6.0%
. 472976
 
4.0%
n 472976
 
4.0%
Other values (14) 4020296
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11824400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 1418928
 
12.0%
o 1182440
 
10.0%
i 709464
 
6.0%
m 709464
 
6.0%
c 709464
 
6.0%
r 709464
 
6.0%
e 709464
 
6.0%
t 709464
 
6.0%
. 472976
 
4.0%
n 472976
 
4.0%
Other values (14) 4020296
34.0%
Distinct17649
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:39.679682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length25
Mean length25.15242211
Min length23

Characters and Unicode

Total characters5948246
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

Unique2980 ?
Unique (%)1.3%

Sample

1st row2018-1-25T18:39:25.000CMT
2nd row2016-3-17T20:16:02.000CMT
3rd row2017-5-29T12:33:23.000CMT
4th row2016-3-17T19:58:48.000CMT
5th row2016-3-17T22:50:34.000CMT
ValueCountFrequency (%)
2019-12-16t13:32:44.000cmt 49
 
< 0.1%
2019-12-16t13:32:37.000cmt 49
 
< 0.1%
2017-8-24t09:06:54.000cmt 49
 
< 0.1%
2019-12-16t13:32:39.000cmt 49
 
< 0.1%
2019-12-16t13:27:24.000cmt 48
 
< 0.1%
2019-12-16t13:27:26.000cmt 48
 
< 0.1%
2019-12-16t13:28:01.000cmt 48
 
< 0.1%
2019-12-16t13:28:17.000cmt 48
 
< 0.1%
2019-12-16t13:32:31.000cmt 48
 
< 0.1%
2019-12-16t13:28:34.000cmt 48
 
< 0.1%
Other values (17639) 236004
99.8%
2025-02-14T15:18:39.832381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1124034
18.9%
1 767092
12.9%
2 578493
9.7%
- 472976
8.0%
T 472976
8.0%
: 472976
8.0%
3 315757
 
5.3%
6 277521
 
4.7%
. 236488
 
4.0%
C 236488
 
4.0%
Other values (6) 993445
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5948246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1124034
18.9%
1 767092
12.9%
2 578493
9.7%
- 472976
8.0%
T 472976
8.0%
: 472976
8.0%
3 315757
 
5.3%
6 277521
 
4.7%
. 236488
 
4.0%
C 236488
 
4.0%
Other values (6) 993445
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5948246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1124034
18.9%
1 767092
12.9%
2 578493
9.7%
- 472976
8.0%
T 472976
8.0%
: 472976
8.0%
3 315757
 
5.3%
6 277521
 
4.7%
. 236488
 
4.0%
C 236488
 
4.0%
Other values (6) 993445
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5948246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1124034
18.9%
1 767092
12.9%
2 578493
9.7%
- 472976
8.0%
T 472976
8.0%
: 472976
8.0%
3 315757
 
5.3%
6 277521
 
4.7%
. 236488
 
4.0%
C 236488
 
4.0%
Other values (6) 993445
16.7%

rightsHolder
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:39.881012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters8277080
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThe Field Museum of Natural History
2nd rowThe Field Museum of Natural History
3rd rowThe Field Museum of Natural History
4th rowThe Field Museum of Natural History
5th rowThe Field Museum of Natural History
ValueCountFrequency (%)
the 236488
16.7%
field 236488
16.7%
museum 236488
16.7%
of 236488
16.7%
natural 236488
16.7%
history 236488
16.7%
2025-02-14T15:18:39.968218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1182440
14.3%
e 709464
 
8.6%
u 709464
 
8.6%
s 472976
 
5.7%
i 472976
 
5.7%
l 472976
 
5.7%
r 472976
 
5.7%
t 472976
 
5.7%
a 472976
 
5.7%
o 472976
 
5.7%
Other values (10) 2364880
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8277080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1182440
14.3%
e 709464
 
8.6%
u 709464
 
8.6%
s 472976
 
5.7%
i 472976
 
5.7%
l 472976
 
5.7%
r 472976
 
5.7%
t 472976
 
5.7%
a 472976
 
5.7%
o 472976
 
5.7%
Other values (10) 2364880
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8277080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1182440
14.3%
e 709464
 
8.6%
u 709464
 
8.6%
s 472976
 
5.7%
i 472976
 
5.7%
l 472976
 
5.7%
r 472976
 
5.7%
t 472976
 
5.7%
a 472976
 
5.7%
o 472976
 
5.7%
Other values (10) 2364880
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8277080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1182440
14.3%
e 709464
 
8.6%
u 709464
 
8.6%
s 472976
 
5.7%
i 472976
 
5.7%
l 472976
 
5.7%
r 472976
 
5.7%
t 472976
 
5.7%
a 472976
 
5.7%
o 472976
 
5.7%
Other values (10) 2364880
28.6%

type
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:39.996925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters3310832
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 rowPhysicalObject
2nd rowPhysicalObject
3rd rowPhysicalObject
4th rowPhysicalObject
5th rowPhysicalObject
ValueCountFrequency (%)
physicalobject 236488
100.0%
2025-02-14T15:18:40.097257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 472976
14.3%
P 236488
 
7.1%
h 236488
 
7.1%
y 236488
 
7.1%
s 236488
 
7.1%
i 236488
 
7.1%
a 236488
 
7.1%
l 236488
 
7.1%
O 236488
 
7.1%
b 236488
 
7.1%
Other values (3) 709464
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3310832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 472976
14.3%
P 236488
 
7.1%
h 236488
 
7.1%
y 236488
 
7.1%
s 236488
 
7.1%
i 236488
 
7.1%
a 236488
 
7.1%
l 236488
 
7.1%
O 236488
 
7.1%
b 236488
 
7.1%
Other values (3) 709464
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3310832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 472976
14.3%
P 236488
 
7.1%
h 236488
 
7.1%
y 236488
 
7.1%
s 236488
 
7.1%
i 236488
 
7.1%
a 236488
 
7.1%
l 236488
 
7.1%
O 236488
 
7.1%
b 236488
 
7.1%
Other values (3) 709464
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3310832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 472976
14.3%
P 236488
 
7.1%
h 236488
 
7.1%
y 236488
 
7.1%
s 236488
 
7.1%
i 236488
 
7.1%
a 236488
 
7.1%
l 236488
 
7.1%
O 236488
 
7.1%
b 236488
 
7.1%
Other values (3) 709464
21.4%

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.127823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters945952
Distinct characters4
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 rowFMNH
2nd rowFMNH
3rd rowFMNH
4th rowFMNH
5th rowFMNH
ValueCountFrequency (%)
fmnh 236488
100.0%
2025-02-14T15:18:40.209757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.239130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1655416
Distinct characters5
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 rowMammals
2nd rowMammals
3rd rowMammals
4th rowMammals
5th rowMammals
ValueCountFrequency (%)
mammals 236488
100.0%
2025-02-14T15:18:40.328826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1655416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1655416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1655416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

datasetID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.366698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters4493272
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 rowmammals-24-sep-2020
2nd rowmammals-24-sep-2020
3rd rowmammals-24-sep-2020
4th rowmammals-24-sep-2020
5th rowmammals-24-sep-2020
ValueCountFrequency (%)
mammals-24-sep-2020 236488
100.0%
2025-02-14T15:18:40.455889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 709464
15.8%
- 709464
15.8%
2 709464
15.8%
a 472976
10.5%
s 472976
10.5%
0 472976
10.5%
l 236488
 
5.3%
4 236488
 
5.3%
e 236488
 
5.3%
p 236488
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4493272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 709464
15.8%
- 709464
15.8%
2 709464
15.8%
a 472976
10.5%
s 472976
10.5%
0 472976
10.5%
l 236488
 
5.3%
4 236488
 
5.3%
e 236488
 
5.3%
p 236488
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4493272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 709464
15.8%
- 709464
15.8%
2 709464
15.8%
a 472976
10.5%
s 472976
10.5%
0 472976
10.5%
l 236488
 
5.3%
4 236488
 
5.3%
e 236488
 
5.3%
p 236488
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4493272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 709464
15.8%
- 709464
15.8%
2 709464
15.8%
a 472976
10.5%
s 472976
10.5%
0 472976
10.5%
l 236488
 
5.3%
4 236488
 
5.3%
e 236488
 
5.3%
p 236488
 
5.3%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.487391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters945952
Distinct characters4
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 rowFMNH
2nd rowFMNH
3rd rowFMNH
4th rowFMNH
5th rowFMNH
ValueCountFrequency (%)
fmnh 236488
100.0%
2025-02-14T15:18:40.569181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.596728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1655416
Distinct characters5
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 rowMammals
2nd rowMammals
3rd rowMammals
4th rowMammals
5th rowMammals
ValueCountFrequency (%)
mammals 236488
100.0%
2025-02-14T15:18:40.673560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1655416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1655416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1655416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 472976
28.6%
m 472976
28.6%
M 236488
14.3%
l 236488
14.3%
s 236488
14.3%

datasetName
Text

Constant  Missing 

Distinct1
Distinct (%)1.2%
Missing236407
Missing (%)> 99.9%
Memory size1.8 MiB
2025-02-14T15:18:40.703829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters2187
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhilippines Natural History
2nd rowPhilippines Natural History
3rd rowPhilippines Natural History
4th rowPhilippines Natural History
5th rowPhilippines Natural History
ValueCountFrequency (%)
philippines 81
33.3%
natural 81
33.3%
history 81
33.3%
2025-02-14T15:18:40.794234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 324
14.8%
162
 
7.4%
t 162
 
7.4%
l 162
 
7.4%
p 162
 
7.4%
s 162
 
7.4%
r 162
 
7.4%
a 162
 
7.4%
o 81
 
3.7%
H 81
 
3.7%
Other values (7) 567
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 324
14.8%
162
 
7.4%
t 162
 
7.4%
l 162
 
7.4%
p 162
 
7.4%
s 162
 
7.4%
r 162
 
7.4%
a 162
 
7.4%
o 81
 
3.7%
H 81
 
3.7%
Other values (7) 567
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 324
14.8%
162
 
7.4%
t 162
 
7.4%
l 162
 
7.4%
p 162
 
7.4%
s 162
 
7.4%
r 162
 
7.4%
a 162
 
7.4%
o 81
 
3.7%
H 81
 
3.7%
Other values (7) 567
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 324
14.8%
162
 
7.4%
t 162
 
7.4%
l 162
 
7.4%
p 162
 
7.4%
s 162
 
7.4%
r 162
 
7.4%
a 162
 
7.4%
o 81
 
3.7%
H 81
 
3.7%
Other values (7) 567
25.9%

ownerInstitutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.823996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters945952
Distinct characters4
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 rowFMNH
2nd rowFMNH
3rd rowFMNH
4th rowFMNH
5th rowFMNH
ValueCountFrequency (%)
fmnh 236488
100.0%
2025-02-14T15:18:40.902819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 945952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 236488
25.0%
M 236488
25.0%
N 236488
25.0%
H 236488
25.0%

basisOfRecord
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:40.929962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4020296
Distinct characters12
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 rowPreservedSpecimen
2nd rowPreservedSpecimen
3rd rowPreservedSpecimen
4th rowPreservedSpecimen
5th rowPreservedSpecimen
ValueCountFrequency (%)
preservedspecimen 236488
100.0%
2025-02-14T15:18:41.011353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1182440
29.4%
r 472976
 
11.8%
P 236488
 
5.9%
s 236488
 
5.9%
v 236488
 
5.9%
d 236488
 
5.9%
S 236488
 
5.9%
p 236488
 
5.9%
c 236488
 
5.9%
i 236488
 
5.9%
Other values (2) 472976
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4020296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1182440
29.4%
r 472976
 
11.8%
P 236488
 
5.9%
s 236488
 
5.9%
v 236488
 
5.9%
d 236488
 
5.9%
S 236488
 
5.9%
p 236488
 
5.9%
c 236488
 
5.9%
i 236488
 
5.9%
Other values (2) 472976
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4020296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1182440
29.4%
r 472976
 
11.8%
P 236488
 
5.9%
s 236488
 
5.9%
v 236488
 
5.9%
d 236488
 
5.9%
S 236488
 
5.9%
p 236488
 
5.9%
c 236488
 
5.9%
i 236488
 
5.9%
Other values (2) 472976
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4020296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1182440
29.4%
r 472976
 
11.8%
P 236488
 
5.9%
s 236488
 
5.9%
v 236488
 
5.9%
d 236488
 
5.9%
S 236488
 
5.9%
p 236488
 
5.9%
c 236488
 
5.9%
i 236488
 
5.9%
Other values (2) 472976
 
11.8%

occurrenceID
Text

Unique 

Distinct236488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:41.114970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters8513568
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236488 ?
Unique (%)100.0%

Sample

1st row0000a9d2-b94b-41c1-9b16-fb32b7885aee
2nd row0000e0a7-9398-4909-a694-14f8f5e43be7
3rd row0002198f-6889-4b1f-a6c7-d82d828df535
4th row000861bb-20aa-4176-98ee-c72d584c7465
5th row0009531c-6464-4c3c-8266-0ca72ef2b800
ValueCountFrequency (%)
0000a9d2-b94b-41c1-9b16-fb32b7885aee 1
 
< 0.1%
0076da22-cf81-4614-8617-b161c7f805fb 1
 
< 0.1%
0038513a-7f3a-4da2-8adb-ee40c1f1b9e9 1
 
< 0.1%
01314245-42c5-4081-ba6a-229583db4ff2 1
 
< 0.1%
00371bf0-a26b-4b81-ab42-e48be0772697 1
 
< 0.1%
0002198f-6889-4b1f-a6c7-d82d828df535 1
 
< 0.1%
000861bb-20aa-4176-98ee-c72d584c7465 1
 
< 0.1%
0009531c-6464-4c3c-8266-0ca72ef2b800 1
 
< 0.1%
00098845-eecd-429e-8d6b-de318c0e07d9 1
 
< 0.1%
000cab5a-c6d2-430e-9c10-4a9cecef17a5 1
 
< 0.1%
Other values (236478) 236478
> 99.9%
2025-02-14T15:18:41.290960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 945952
 
11.1%
4 679573
 
8.0%
9 503870
 
5.9%
b 503288
 
5.9%
8 502430
 
5.9%
a 501803
 
5.9%
6 444344
 
5.2%
c 443930
 
5.2%
f 443878
 
5.2%
1 443744
 
5.2%
Other values (7) 3100756
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8513568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 945952
 
11.1%
4 679573
 
8.0%
9 503870
 
5.9%
b 503288
 
5.9%
8 502430
 
5.9%
a 501803
 
5.9%
6 444344
 
5.2%
c 443930
 
5.2%
f 443878
 
5.2%
1 443744
 
5.2%
Other values (7) 3100756
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8513568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 945952
 
11.1%
4 679573
 
8.0%
9 503870
 
5.9%
b 503288
 
5.9%
8 502430
 
5.9%
a 501803
 
5.9%
6 444344
 
5.2%
c 443930
 
5.2%
f 443878
 
5.2%
1 443744
 
5.2%
Other values (7) 3100756
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8513568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 945952
 
11.1%
4 679573
 
8.0%
9 503870
 
5.9%
b 503288
 
5.9%
8 502430
 
5.9%
a 501803
 
5.9%
6 444344
 
5.2%
c 443930
 
5.2%
f 443878
 
5.2%
1 443744
 
5.2%
Other values (7) 3100756
36.4%

catalogNumber
Text

Unique 

Distinct236488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:41.514611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.537798958
Min length1

Characters and Unicode

Total characters1309623
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

Unique236488 ?
Unique (%)100.0%

Sample

1st row220155
2nd row33016
3rd row125508
4th row152941
5th row157731
ValueCountFrequency (%)
220155 1
 
< 0.1%
90851 1
 
< 0.1%
133342 1
 
< 0.1%
206917 1
 
< 0.1%
206 1
 
< 0.1%
125508 1
 
< 0.1%
152941 1
 
< 0.1%
157731 1
 
< 0.1%
70409 1
 
< 0.1%
164626 1
 
< 0.1%
Other values (236478) 236478
> 99.9%
2025-02-14T15:18:41.807512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 224503
17.1%
2 162486
12.4%
3 122345
9.3%
5 114804
8.8%
6 114786
8.8%
4 114761
8.8%
8 114189
8.7%
7 114084
8.7%
0 113906
8.7%
9 113759
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1309623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 224503
17.1%
2 162486
12.4%
3 122345
9.3%
5 114804
8.8%
6 114786
8.8%
4 114761
8.8%
8 114189
8.7%
7 114084
8.7%
0 113906
8.7%
9 113759
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1309623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 224503
17.1%
2 162486
12.4%
3 122345
9.3%
5 114804
8.8%
6 114786
8.8%
4 114761
8.8%
8 114189
8.7%
7 114084
8.7%
0 113906
8.7%
9 113759
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1309623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 224503
17.1%
2 162486
12.4%
3 122345
9.3%
5 114804
8.8%
6 114786
8.8%
4 114761
8.8%
8 114189
8.7%
7 114084
8.7%
0 113906
8.7%
9 113759
8.7%

recordNumber
Text

Missing 

Distinct142702
Distinct (%)64.5%
Missing15120
Missing (%)6.4%
Memory size1.8 MiB
2025-02-14T15:18:41.989631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length14
Mean length5.827093347
Min length1

Characters and Unicode

Total characters1289932
Distinct characters72
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

Unique131921 ?
Unique (%)59.6%

Sample

1st rowBDP-5913
2nd row184
3rd rowDW-1091
4th rowBJH-3023
5th rowJCK-3266
ValueCountFrequency (%)
czs 864
 
0.4%
1 347
 
0.2%
2 342
 
0.2%
4 310
 
0.1%
3 310
 
0.1%
7 265
 
0.1%
5 260
 
0.1%
6 245
 
0.1%
8 236
 
0.1%
bmnh 232
 
0.1%
Other values (142547) 218095
98.5%
2025-02-14T15:18:42.222508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 125374
 
9.7%
- 120150
 
9.3%
2 93051
 
7.2%
3 85402
 
6.6%
4 80587
 
6.2%
5 78815
 
6.1%
6 73251
 
5.7%
7 71050
 
5.5%
8 67256
 
5.2%
0 66049
 
5.1%
Other values (62) 428947
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1289932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 125374
 
9.7%
- 120150
 
9.3%
2 93051
 
7.2%
3 85402
 
6.6%
4 80587
 
6.2%
5 78815
 
6.1%
6 73251
 
5.7%
7 71050
 
5.5%
8 67256
 
5.2%
0 66049
 
5.1%
Other values (62) 428947
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1289932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 125374
 
9.7%
- 120150
 
9.3%
2 93051
 
7.2%
3 85402
 
6.6%
4 80587
 
6.2%
5 78815
 
6.1%
6 73251
 
5.7%
7 71050
 
5.5%
8 67256
 
5.2%
0 66049
 
5.1%
Other values (62) 428947
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1289932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 125374
 
9.7%
- 120150
 
9.3%
2 93051
 
7.2%
3 85402
 
6.6%
4 80587
 
6.2%
5 78815
 
6.1%
6 73251
 
5.7%
7 71050
 
5.5%
8 67256
 
5.2%
0 66049
 
5.1%
Other values (62) 428947
33.3%

recordedBy
Text

Missing 

Distinct2754
Distinct (%)1.2%
Missing2691
Missing (%)1.1%
Memory size1.8 MiB
2025-02-14T15:18:42.362725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length92
Median length64
Mean length13.7017327
Min length2

Characters and Unicode

Total characters3203424
Distinct characters65
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

Unique1047 ?
Unique (%)0.4%

Sample

1st rowB. D. Patterson
2nd rowH. Stevens
3rd rowD. E. Willard
4th rowB. J. Hayward
5th rowJ. C. Kerbis
ValueCountFrequency (%)
h 57339
 
8.4%
c 32915
 
4.8%
j 30464
 
4.4%
w 29705
 
4.3%
m 27556
 
4.0%
t 25140
 
3.7%
d 23822
 
3.5%
s 23681
 
3.4%
l 20590
 
3.0%
e 19909
 
2.9%
Other values (2428) 395429
57.6%
2025-02-14T15:18:42.569196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
452753
 
14.1%
. 383464
 
12.0%
e 213411
 
6.7%
a 198037
 
6.2%
o 172781
 
5.4%
r 137271
 
4.3%
t 131845
 
4.1%
l 127018
 
4.0%
n 124235
 
3.9%
i 107238
 
3.3%
Other values (55) 1155371
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3203424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
452753
 
14.1%
. 383464
 
12.0%
e 213411
 
6.7%
a 198037
 
6.2%
o 172781
 
5.4%
r 137271
 
4.3%
t 131845
 
4.1%
l 127018
 
4.0%
n 124235
 
3.9%
i 107238
 
3.3%
Other values (55) 1155371
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3203424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
452753
 
14.1%
. 383464
 
12.0%
e 213411
 
6.7%
a 198037
 
6.2%
o 172781
 
5.4%
r 137271
 
4.3%
t 131845
 
4.1%
l 127018
 
4.0%
n 124235
 
3.9%
i 107238
 
3.3%
Other values (55) 1155371
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3203424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
452753
 
14.1%
. 383464
 
12.0%
e 213411
 
6.7%
a 198037
 
6.2%
o 172781
 
5.4%
r 137271
 
4.3%
t 131845
 
4.1%
l 127018
 
4.0%
n 124235
 
3.9%
i 107238
 
3.3%
Other values (55) 1155371
36.1%

sex
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing16090
Missing (%)6.8%
Memory size1.8 MiB
2025-02-14T15:18:42.609792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.958779118
Min length1

Characters and Unicode

Total characters1092905
Distinct characters9
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 (%)< 0.1%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale
ValueCountFrequency (%)
male 114699
52.0%
female 105682
48.0%
m 8
 
< 0.1%
5
 
< 0.1%
f 3
 
< 0.1%
j 1
 
< 0.1%
2025-02-14T15:18:42.697619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 326063
29.8%
a 220381
20.2%
l 220381
20.2%
M 114703
 
10.5%
m 105686
 
9.7%
F 105682
 
9.7%
? 5
 
< 0.1%
f 3
 
< 0.1%
j 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1092905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 326063
29.8%
a 220381
20.2%
l 220381
20.2%
M 114703
 
10.5%
m 105686
 
9.7%
F 105682
 
9.7%
? 5
 
< 0.1%
f 3
 
< 0.1%
j 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1092905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 326063
29.8%
a 220381
20.2%
l 220381
20.2%
M 114703
 
10.5%
m 105686
 
9.7%
F 105682
 
9.7%
? 5
 
< 0.1%
f 3
 
< 0.1%
j 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1092905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 326063
29.8%
a 220381
20.2%
l 220381
20.2%
M 114703
 
10.5%
m 105686
 
9.7%
F 105682
 
9.7%
? 5
 
< 0.1%
f 3
 
< 0.1%
j 1
 
< 0.1%
Distinct72
Distinct (%)< 0.1%
Missing524
Missing (%)0.2%
Memory size1.8 MiB
2025-02-14T15:18:42.734182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length23
Mean length9.128286518
Min length3

Characters and Unicode

Total characters2153947
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowskull,asr,tiss
2nd rowskin,skull
3rd rowalc
4th rowskin,skull
5th rowskull,asr
ValueCountFrequency (%)
skin,skull 82777
35.1%
alc 43548
18.5%
alc,tiss 17083
 
7.2%
skull,asr,tiss 15159
 
6.4%
skull 14029
 
5.9%
skull,skel 12958
 
5.5%
skin,skull,skel 9853
 
4.2%
skull,asr 9543
 
4.0%
skin,skull,skel,tiss 8206
 
3.5%
skull,skel,tiss 6825
 
2.9%
Other values (62) 15983
 
6.8%
2025-02-14T15:18:42.936398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 444963
20.7%
l 429307
19.9%
k 314813
14.6%
, 225609
10.5%
u 166180
 
7.7%
i 164139
 
7.6%
n 114447
 
5.3%
a 91955
 
4.3%
c 70183
 
3.3%
t 54211
 
2.5%
Other values (7) 78140
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2153947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 444963
20.7%
l 429307
19.9%
k 314813
14.6%
, 225609
10.5%
u 166180
 
7.7%
i 164139
 
7.6%
n 114447
 
5.3%
a 91955
 
4.3%
c 70183
 
3.3%
t 54211
 
2.5%
Other values (7) 78140
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2153947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 444963
20.7%
l 429307
19.9%
k 314813
14.6%
, 225609
10.5%
u 166180
 
7.7%
i 164139
 
7.6%
n 114447
 
5.3%
a 91955
 
4.3%
c 70183
 
3.3%
t 54211
 
2.5%
Other values (7) 78140
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2153947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 444963
20.7%
l 429307
19.9%
k 314813
14.6%
, 225609
10.5%
u 166180
 
7.7%
i 164139
 
7.6%
n 114447
 
5.3%
a 91955
 
4.3%
c 70183
 
3.3%
t 54211
 
2.5%
Other values (7) 78140
 
3.6%

associatedMedia
Text

Missing 

Distinct913
Distinct (%)90.6%
Missing235480
Missing (%)99.6%
Memory size1.8 MiB
2025-02-14T15:18:42.969944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length255
Mean length204.9920635
Min length57

Characters and Unicode

Total characters206632
Distinct characters65
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

Unique884 ?
Unique (%)87.7%

Sample

1st rowhttp://fm-digital-assets.fieldmuseum.org/722/425/43815_DLowerJaw_RB01_MZ.jpg|http://fm-digital-assets.fieldmuseum.org/722/426/43815_Dorsal_RB01_MZ.jpg|http://fm-digital-assets.fieldmuseum.org/722/427/43815_FullLateral_RB01_MZ.jpg|http://fm-digital-assets.
2nd rowhttp://fm-digital-assets.fieldmuseum.org/533/855/Z78576.jpg|http://fm-digital-assets.fieldmuseum.org/533/858/Z78604.jpg
3rd rowhttp://fm-digital-assets.fieldmuseum.org/721/433/85244_DLowerJaw_PL01_MZ.jpg|http://fm-digital-assets.fieldmuseum.org/721/434/85244_Dorsal_PL01_MZ.jpg|http://fm-digital-assets.fieldmuseum.org/721/435/85244_FullLateral_PL01_MZ.jpg|http://fm-digital-assets.
4th rowhttp://fm-digital-assets.fieldmuseum.org/534/099/Z92868c.jpg|http://fm-digital-assets.fieldmuseum.org/534/374/CSZ58019.jpg|http://fm-digital-assets.fieldmuseum.org/534/439/z32t.JPG
5th rowhttp://fm-digital-assets.fieldmuseum.org/721/207/17501_DLowerJaw_PL01_MZ.jpg|http://fm-digital-assets.fieldmuseum.org/721/208/17501_Dorsal_PL01_MZ.jpg|http://fm-digital-assets.fieldmuseum.org/721/209/17501_FullLateral_PL01_MZ.jpg|http://fm-digital-assets.
ValueCountFrequency (%)
http://fm-digital-assets.fieldmuseum.org/533/846/z13t.jpg|http://fm-digital-assets.fieldmuseum.org/534/110/z93889c.jpg|http://fm-digital-assets.fieldmuseum.org/534/122/z93886_2c.jpg|http://fm-digital-assets.fieldmuseum.org/534/400/csz62847.jpg|http://fm-d 8
 
0.8%
http://fm-digital-assets.fieldmuseum.org/534/252/csz19942.jpg|http://fm-digital-assets.fieldmuseum.org/534/253/csz19943.jpg|http://fm-digital-assets.fieldmuseum.org/534/254/csz19944.jpg|http://fm-digital-assets.fieldmuseum.org/534/255/csz19945.jpg|http 7
 
0.7%
http://fm-digital-assets.fieldmuseum.org/534/414/csz49870.jpg 7
 
0.7%
http://fm-digital-assets.fieldmuseum.org/1395/444/z94491_307_2c.jpg 7
 
0.7%
http://fm-digital-assets.fieldmuseum.org/534/099/z92868c.jpg|http://fm-digital-assets.fieldmuseum.org/534/374/csz58019.jpg|http://fm-digital-assets.fieldmuseum.org/534/439/z32t.jpg 6
 
0.6%
http://fm-digital-assets.fieldmuseum.org/533/821/gn86222c.jpg|http://fm-digital-assets.fieldmuseum.org/534/387/csz71175.jpg|http://fm-digital-assets.fieldmuseum.org/534/410/csz49557.jpg|http://fm-digital-assets.fieldmuseum.org/534/411/csz77755.jpg|http 6
 
0.6%
http://fm-digital-assets.fieldmuseum.org/533/855/z78576.jpg|http://fm-digital-assets.fieldmuseum.org/533/858/z78604.jpg 6
 
0.6%
http://fm-digital-assets.fieldmuseum.org/534/409/csz77110.jpg 5
 
0.5%
http://fm-digital-assets.fieldmuseum.org/533/822/gn86223c.jpg|http://fm-digital-assets.fieldmuseum.org/534/288/gn86239_1c.jpg|http://fm-digital-assets.fieldmuseum.org/534/289/gn86239_5c.jpg|http://fm-digital-assets.fieldmuseum.org/534/290/gn86239_7c.jpg|h 5
 
0.5%
http://fm-digital-assets.fieldmuseum.org/534/388/csz71436.jpg 5
 
0.5%
Other values (907) 950
93.9%
2025-02-14T15:18:43.081761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 14382
 
7.0%
t 13324
 
6.4%
s 12538
 
6.1%
e 9793
 
4.7%
i 9297
 
4.5%
a 9022
 
4.4%
m 8616
 
4.2%
g 8331
 
4.0%
l 8279
 
4.0%
. 8116
 
3.9%
Other values (55) 104934
50.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 206632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 14382
 
7.0%
t 13324
 
6.4%
s 12538
 
6.1%
e 9793
 
4.7%
i 9297
 
4.5%
a 9022
 
4.4%
m 8616
 
4.2%
g 8331
 
4.0%
l 8279
 
4.0%
. 8116
 
3.9%
Other values (55) 104934
50.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 206632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 14382
 
7.0%
t 13324
 
6.4%
s 12538
 
6.1%
e 9793
 
4.7%
i 9297
 
4.5%
a 9022
 
4.4%
m 8616
 
4.2%
g 8331
 
4.0%
l 8279
 
4.0%
. 8116
 
3.9%
Other values (55) 104934
50.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 206632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 14382
 
7.0%
t 13324
 
6.4%
s 12538
 
6.1%
e 9793
 
4.7%
i 9297
 
4.5%
a 9022
 
4.4%
m 8616
 
4.2%
g 8331
 
4.0%
l 8279
 
4.0%
. 8116
 
3.9%
Other values (55) 104934
50.8%

associatedSequences
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing236487
Missing (%)> 99.9%
Memory size1.8 MiB
2025-02-14T15:18:43.108875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters6
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 (%)100.0%

Sample

1st rowGU731517
ValueCountFrequency (%)
gu731517 1
100.0%
2025-02-14T15:18:43.280271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 2
25.0%
1 2
25.0%
G 1
12.5%
U 1
12.5%
3 1
12.5%
5 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 2
25.0%
1 2
25.0%
G 1
12.5%
U 1
12.5%
3 1
12.5%
5 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 2
25.0%
1 2
25.0%
G 1
12.5%
U 1
12.5%
3 1
12.5%
5 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 2
25.0%
1 2
25.0%
G 1
12.5%
U 1
12.5%
3 1
12.5%
5 1
12.5%

organismID
Text

Unique 

Distinct236488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-02-14T15:18:43.547258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.972565204
Min length6

Characters and Unicode

Total characters1648928
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

Unique236488 ?
Unique (%)100.0%

Sample

1st row2377119
2nd row2532242
3rd row2624276
4th row2651709
5th row2656499
ValueCountFrequency (%)
2377119 1
 
< 0.1%
2589721 1
 
< 0.1%
2632110 1
 
< 0.1%
706944 1
 
< 0.1%
2500205 1
 
< 0.1%
2624276 1
 
< 0.1%
2651709 1
 
< 0.1%
2656499 1
 
< 0.1%
2569634 1
 
< 0.1%
2663394 1
 
< 0.1%
Other values (236478) 236478
> 99.9%
2025-02-14T15:18:43.801451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 331954
20.1%
5 220043
13.3%
6 219376
13.3%
1 136601
8.3%
3 127751
 
7.7%
7 126759
 
7.7%
8 125053
 
7.6%
0 121640
 
7.4%
9 120656
 
7.3%
4 119095
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1648928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 331954
20.1%
5 220043
13.3%
6 219376
13.3%
1 136601
8.3%
3 127751
 
7.7%
7 126759
 
7.7%
8 125053
 
7.6%
0 121640
 
7.4%
9 120656
 
7.3%
4 119095
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1648928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 331954
20.1%
5 220043
13.3%
6 219376
13.3%
1 136601
8.3%
3 127751
 
7.7%
7 126759
 
7.7%
8 125053
 
7.6%
0 121640
 
7.4%
9 120656
 
7.3%
4 119095
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1648928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 331954
20.1%
5 220043
13.3%
6 219376
13.3%
1 136601
8.3%
3 127751
 
7.7%
7 126759
 
7.7%
8 125053
 
7.6%
0 121640
 
7.4%
9 120656
 
7.3%
4 119095
 
7.2%

fieldNumber
Text

Missing 

Distinct205288
Distinct (%)92.5%
Missing14577
Missing (%)6.2%
Memory size1.8 MiB
2025-02-14T15:18:44.013864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.445944545
Min length1

Characters and Unicode

Total characters1208515
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

Unique188665 ?
Unique (%)85.0%

Sample

1st row220219
2nd row64524
3rd row127331
4th row49591
5th row224542
ValueCountFrequency (%)
2415 2
 
< 0.1%
1576 2
 
< 0.1%
14198 2
 
< 0.1%
10046 2
 
< 0.1%
11013 2
 
< 0.1%
16541 2
 
< 0.1%
11020 2
 
< 0.1%
15271 2
 
< 0.1%
11846 2
 
< 0.1%
3014 2
 
< 0.1%
Other values (205278) 221891
> 99.9%
2025-02-14T15:18:44.262798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 216020
17.9%
2 148794
12.3%
3 108780
9.0%
6 108743
9.0%
5 107456
8.9%
7 107391
8.9%
4 106809
8.8%
8 104058
8.6%
0 102449
8.5%
9 98015
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1208515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 216020
17.9%
2 148794
12.3%
3 108780
9.0%
6 108743
9.0%
5 107456
8.9%
7 107391
8.9%
4 106809
8.8%
8 104058
8.6%
0 102449
8.5%
9 98015
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1208515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 216020
17.9%
2 148794
12.3%
3 108780
9.0%
6 108743
9.0%
5 107456
8.9%
7 107391
8.9%
4 106809
8.8%
8 104058
8.6%
0 102449
8.5%
9 98015
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1208515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 216020
17.9%
2 148794
12.3%
3 108780
9.0%
6 108743
9.0%
5 107456
8.9%
7 107391
8.9%
4 106809
8.8%
8 104058
8.6%
0 102449
8.5%
9 98015
8.1%

startDayOfYear
Text

Missing 

Distinct31
Distinct (%)< 0.1%
Missing17050
Missing (%)7.2%
Memory size1.8 MiB
2025-02-14T15:18:44.322374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.711936857
Min length1

Characters and Unicode

Total characters375664
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 row22
2nd row11
3rd row29
4th row10
5th row13
ValueCountFrequency (%)
28 7963
 
3.6%
22 7893
 
3.6%
27 7862
 
3.6%
26 7622
 
3.5%
14 7577
 
3.5%
25 7532
 
3.4%
23 7459
 
3.4%
20 7443
 
3.4%
10 7388
 
3.4%
8 7366
 
3.4%
Other values (21) 143333
65.3%
2025-02-14T15:18:44.426646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

year
Text

Missing 

Distinct158
Distinct (%)0.1%
Missing7781
Missing (%)3.3%
Memory size1.8 MiB
2025-02-14T15:18:44.525950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.999995628
Min length3

Characters and Unicode

Total characters914827
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

Unique12 ?
Unique (%)< 0.1%

Sample

1st row2012
2nd row1929
3rd row1983
4th row1965
5th row1996
ValueCountFrequency (%)
2007 5395
 
2.4%
1963 4805
 
2.1%
2006 4715
 
2.1%
2005 4463
 
2.0%
1950 4416
 
1.9%
2004 4334
 
1.9%
1965 4256
 
1.9%
2012 4114
 
1.8%
2011 4073
 
1.8%
2009 3602
 
1.6%
Other values (148) 184534
80.7%
2025-02-14T15:18:44.694576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 220490
24.1%
9 213254
23.3%
0 129703
14.2%
2 100395
11.0%
5 50664
 
5.5%
6 49241
 
5.4%
3 39812
 
4.4%
8 38512
 
4.2%
4 37710
 
4.1%
7 35046
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 914827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 220490
24.1%
9 213254
23.3%
0 129703
14.2%
2 100395
11.0%
5 50664
 
5.5%
6 49241
 
5.4%
3 39812
 
4.4%
8 38512
 
4.2%
4 37710
 
4.1%
7 35046
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 914827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 220490
24.1%
9 213254
23.3%
0 129703
14.2%
2 100395
11.0%
5 50664
 
5.5%
6 49241
 
5.4%
3 39812
 
4.4%
8 38512
 
4.2%
4 37710
 
4.1%
7 35046
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 914827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 220490
24.1%
9 213254
23.3%
0 129703
14.2%
2 100395
11.0%
5 50664
 
5.5%
6 49241
 
5.4%
3 39812
 
4.4%
8 38512
 
4.2%
4 37710
 
4.1%
7 35046
 
3.8%

month
Text

Missing 

Distinct12
Distinct (%)< 0.1%
Missing10882
Missing (%)4.6%
Memory size1.8 MiB
2025-02-14T15:18:44.739223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.223779509
Min length1

Characters and Unicode

Total characters276092
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 row9
2nd row3
3rd row10
4th row6
5th row3
ValueCountFrequency (%)
7 27371
12.1%
8 26587
11.8%
3 21809
9.7%
5 19217
8.5%
11 18727
8.3%
10 18197
8.1%
4 17978
8.0%
6 17774
7.9%
2 15699
7.0%
9 15568
6.9%
Other values (2) 26679
11.8%
2025-02-14T15:18:44.837400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 82330
29.8%
2 29261
 
10.6%
7 27371
 
9.9%
8 26587
 
9.6%
3 21809
 
7.9%
5 19217
 
7.0%
0 18197
 
6.6%
4 17978
 
6.5%
6 17774
 
6.4%
9 15568
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 276092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 82330
29.8%
2 29261
 
10.6%
7 27371
 
9.9%
8 26587
 
9.6%
3 21809
 
7.9%
5 19217
 
7.0%
0 18197
 
6.6%
4 17978
 
6.5%
6 17774
 
6.4%
9 15568
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 276092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 82330
29.8%
2 29261
 
10.6%
7 27371
 
9.9%
8 26587
 
9.6%
3 21809
 
7.9%
5 19217
 
7.0%
0 18197
 
6.6%
4 17978
 
6.5%
6 17774
 
6.4%
9 15568
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 276092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 82330
29.8%
2 29261
 
10.6%
7 27371
 
9.9%
8 26587
 
9.6%
3 21809
 
7.9%
5 19217
 
7.0%
0 18197
 
6.6%
4 17978
 
6.5%
6 17774
 
6.4%
9 15568
 
5.6%

day
Text

Missing 

Distinct31
Distinct (%)< 0.1%
Missing17050
Missing (%)7.2%
Memory size1.8 MiB
2025-02-14T15:18:44.881442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.711936857
Min length1

Characters and Unicode

Total characters375664
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 row22
2nd row11
3rd row29
4th row10
5th row13
ValueCountFrequency (%)
28 7963
 
3.6%
22 7893
 
3.6%
27 7862
 
3.6%
26 7622
 
3.5%
14 7577
 
3.5%
25 7532
 
3.4%
23 7459
 
3.4%
20 7443
 
3.4%
10 7388
 
3.4%
8 7366
 
3.4%
Other values (21) 143333
65.3%
2025-02-14T15:18:44.989255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 96727
25.7%
1 96594
25.7%
3 32174
 
8.6%
8 22174
 
5.9%
5 21854
 
5.8%
7 21852
 
5.8%
4 21840
 
5.8%
6 21183
 
5.6%
0 21153
 
5.6%
9 20113
 
5.4%
Distinct18426
Distinct (%)7.8%
Missing305
Missing (%)0.1%
Memory size1.8 MiB
2025-02-14T15:18:45.137375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length185
Median length132
Mean length75.91310552
Min length3

Characters and Unicode

Total characters17929385
Distinct characters95
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

Unique7026 ?
Unique (%)3.0%

Sample

1st rowAfrica, Kenya, Coastal Prov, Kwale Dist, Ethiopean: Shimoni, "Slave Cave"
2nd rowAsia, China, Yunnan, Lijiang Pref, Palearctic: Dayan Co; Lijiang Range
3rd rowSouth America, Peru, Madre de Dios, Manu, Neotropics: Hacienda Amazonia
4th rowAfrica, Kenya, Rift Valley Prov, Nakuru Dist, Ethiopean: Naivasha, 2 mi SE; Lake Naivasha, Lake Hotel
5th rowAfrica, Uganda, Kigezi, Kisoro, Ethiopean: Mgahinga Gorilla Nat Pk, edge of encroached area and Pk, along pipeline
ValueCountFrequency (%)
america 97988
 
3.8%
africa 80049
 
3.1%
km 71602
 
2.8%
ethiopean 65083
 
2.5%
south 54352
 
2.1%
north 54066
 
2.1%
neotropics 53840
 
2.1%
asia 49239
 
1.9%
nearctic 43152
 
1.7%
usa 36933
 
1.4%
Other values (17062) 1957314
76.3%
2025-02-14T15:18:45.382560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2328148
 
13.0%
a 1783801
 
9.9%
i 1240060
 
6.9%
, 1031250
 
5.8%
e 1009729
 
5.6%
o 975676
 
5.4%
r 961678
 
5.4%
n 811309
 
4.5%
t 739162
 
4.1%
c 560993
 
3.1%
Other values (85) 6487579
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17929385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2328148
 
13.0%
a 1783801
 
9.9%
i 1240060
 
6.9%
, 1031250
 
5.8%
e 1009729
 
5.6%
o 975676
 
5.4%
r 961678
 
5.4%
n 811309
 
4.5%
t 739162
 
4.1%
c 560993
 
3.1%
Other values (85) 6487579
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17929385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2328148
 
13.0%
a 1783801
 
9.9%
i 1240060
 
6.9%
, 1031250
 
5.8%
e 1009729
 
5.6%
o 975676
 
5.4%
r 961678
 
5.4%
n 811309
 
4.5%
t 739162
 
4.1%
c 560993
 
3.1%
Other values (85) 6487579
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17929385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2328148
 
13.0%
a 1783801
 
9.9%
i 1240060
 
6.9%
, 1031250
 
5.8%
e 1009729
 
5.6%
o 975676
 
5.4%
r 961678
 
5.4%
n 811309
 
4.5%
t 739162
 
4.1%
c 560993
 
3.1%
Other values (85) 6487579
36.2%

continent
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing4336
Missing (%)1.8%
Memory size1.8 MiB
2025-02-14T15:18:45.426119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.580740205
Min length4

Characters and Unicode

Total characters1992036
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAsia
3rd rowSouth America
4th rowAfrica
5th rowAfrica
ValueCountFrequency (%)
america 99129
29.9%
africa 79525
24.0%
north 51517
15.6%
asia 49120
14.8%
south 47612
14.4%
oceania 3401
 
1.0%
europe 962
 
0.3%
antarctica 15
 
< 0.1%
2025-02-14T15:18:45.508162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 234606
11.8%
i 231190
11.6%
r 231148
11.6%
A 227789
11.4%
c 182085
9.1%
e 103492
 
5.2%
o 100091
 
5.0%
t 99159
 
5.0%
m 99129
 
5.0%
99129
 
5.0%
Other values (10) 384218
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1992036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 234606
11.8%
i 231190
11.6%
r 231148
11.6%
A 227789
11.4%
c 182085
9.1%
e 103492
 
5.2%
o 100091
 
5.0%
t 99159
 
5.0%
m 99129
 
5.0%
99129
 
5.0%
Other values (10) 384218
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1992036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 234606
11.8%
i 231190
11.6%
r 231148
11.6%
A 227789
11.4%
c 182085
9.1%
e 103492
 
5.2%
o 100091
 
5.0%
t 99159
 
5.0%
m 99129
 
5.0%
99129
 
5.0%
Other values (10) 384218
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1992036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 234606
11.8%
i 231190
11.6%
r 231148
11.6%
A 227789
11.4%
c 182085
9.1%
e 103492
 
5.2%
o 100091
 
5.0%
t 99159
 
5.0%
m 99129
 
5.0%
99129
 
5.0%
Other values (10) 384218
19.3%

waterBody
Text

Missing 

Distinct3
Distinct (%)14.3%
Missing236467
Missing (%)> 99.9%
Memory size1.8 MiB
2025-02-14T15:18:45.536263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.857142857
Min length6

Characters and Unicode

Total characters144
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

Unique1 ?
Unique (%)4.8%

Sample

1st rowPacific
2nd rowPacific
3rd rowArctic
4th rowPacific
5th rowPacific
ValueCountFrequency (%)
pacific 16
76.2%
arctic 4
 
19.0%
atlantic 1
 
4.8%
2025-02-14T15:18:45.626890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 41
28.5%
i 37
25.7%
a 17
11.8%
P 16
 
11.1%
f 16
 
11.1%
t 6
 
4.2%
A 5
 
3.5%
r 4
 
2.8%
l 1
 
0.7%
n 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 41
28.5%
i 37
25.7%
a 17
11.8%
P 16
 
11.1%
f 16
 
11.1%
t 6
 
4.2%
A 5
 
3.5%
r 4
 
2.8%
l 1
 
0.7%
n 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 41
28.5%
i 37
25.7%
a 17
11.8%
P 16
 
11.1%
f 16
 
11.1%
t 6
 
4.2%
A 5
 
3.5%
r 4
 
2.8%
l 1
 
0.7%
n 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 41
28.5%
i 37
25.7%
a 17
11.8%
P 16
 
11.1%
f 16
 
11.1%
t 6
 
4.2%
A 5
 
3.5%
r 4
 
2.8%
l 1
 
0.7%
n 1
 
0.7%

islandGroup
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing235141
Missing (%)99.4%
Memory size1.8 MiB
2025-02-14T15:18:45.657393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.06087602
Min length11

Characters and Unicode

Total characters14899
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 rowWest Indies
2nd rowWest Indies
3rd rowWest Indies
4th rowWest Indies
5th rowWest Indies
ValueCountFrequency (%)
west 1291
47.9%
indies 1291
47.9%
new 34
 
1.3%
georgia 34
 
1.3%
islands 22
 
0.8%
solomon 16
 
0.6%
russel 6
 
0.2%
2025-02-14T15:18:45.755169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2656
17.8%
s 2638
17.7%
1347
9.0%
n 1329
8.9%
i 1325
8.9%
I 1313
8.8%
d 1313
8.8%
W 1291
8.7%
t 1291
8.7%
o 82
 
0.6%
Other values (11) 314
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2656
17.8%
s 2638
17.7%
1347
9.0%
n 1329
8.9%
i 1325
8.9%
I 1313
8.8%
d 1313
8.8%
W 1291
8.7%
t 1291
8.7%
o 82
 
0.6%
Other values (11) 314
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2656
17.8%
s 2638
17.7%
1347
9.0%
n 1329
8.9%
i 1325
8.9%
I 1313
8.8%
d 1313
8.8%
W 1291
8.7%
t 1291
8.7%
o 82
 
0.6%
Other values (11) 314
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2656
17.8%
s 2638
17.7%
1347
9.0%
n 1329
8.9%
i 1325
8.9%
I 1313
8.8%
d 1313
8.8%
W 1291
8.7%
t 1291
8.7%
o 82
 
0.6%
Other values (11) 314
 
2.1%

island
Text

Missing 

Distinct60
Distinct (%)3.9%
Missing234964
Missing (%)99.4%
Memory size1.8 MiB
2025-02-14T15:18:45.788916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length20
Mean length8.739501312
Min length4

Characters and Unicode

Total characters13319
Distinct characters45
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

Unique13 ?
Unique (%)0.9%

Sample

1st rowTrinidad
2nd rowTrinidad
3rd rowCuraçao
4th rowTrinidad
5th rowLong
ValueCountFrequency (%)
trinidad 601
30.4%
island 245
12.4%
long 170
 
8.6%
cuba 125
 
6.3%
curaçao 85
 
4.3%
hispaniola 70
 
3.5%
new 62
 
3.1%
vella 61
 
3.1%
guadalcanal 58
 
2.9%
georgia 45
 
2.3%
Other values (60) 457
23.1%
2025-02-14T15:18:45.889710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2087
15.7%
d 1586
11.9%
i 1535
11.5%
n 1430
10.7%
r 852
 
6.4%
l 682
 
5.1%
T 609
 
4.6%
o 552
 
4.1%
455
 
3.4%
s 373
 
2.8%
Other values (35) 3158
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2087
15.7%
d 1586
11.9%
i 1535
11.5%
n 1430
10.7%
r 852
 
6.4%
l 682
 
5.1%
T 609
 
4.6%
o 552
 
4.1%
455
 
3.4%
s 373
 
2.8%
Other values (35) 3158
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2087
15.7%
d 1586
11.9%
i 1535
11.5%
n 1430
10.7%
r 852
 
6.4%
l 682
 
5.1%
T 609
 
4.6%
o 552
 
4.1%
455
 
3.4%
s 373
 
2.8%
Other values (35) 3158
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2087
15.7%
d 1586
11.9%
i 1535
11.5%
n 1430
10.7%
r 852
 
6.4%
l 682
 
5.1%
T 609
 
4.6%
o 552
 
4.1%
455
 
3.4%
s 373
 
2.8%
Other values (35) 3158
23.7%
Distinct211
Distinct (%)0.1%
Missing356
Missing (%)0.2%
Memory size1.8 MiB
2025-02-14T15:18:46.023214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length6.619695763
Min length3

Characters and Unicode

Total characters1563122
Distinct characters57
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

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowKenya
2nd rowChina
3rd rowPeru
4th rowKenya
5th rowUganda
ValueCountFrequency (%)
usa 36917
 
14.0%
is 20084
 
7.6%
philippine 19525
 
7.4%
tanzania 14272
 
5.4%
peru 13705
 
5.2%
madagascar 11230
 
4.3%
kenya 10669
 
4.1%
egypt 9063
 
3.4%
uganda 8063
 
3.1%
chile 7637
 
2.9%
Other values (219) 112195
42.6%
2025-02-14T15:18:46.227496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 244818
 
15.7%
i 162439
 
10.4%
n 116220
 
7.4%
e 85613
 
5.5%
l 66194
 
4.2%
r 58161
 
3.7%
o 56672
 
3.6%
p 52497
 
3.4%
s 47810
 
3.1%
U 46046
 
2.9%
Other values (47) 626652
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1563122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 244818
 
15.7%
i 162439
 
10.4%
n 116220
 
7.4%
e 85613
 
5.5%
l 66194
 
4.2%
r 58161
 
3.7%
o 56672
 
3.6%
p 52497
 
3.4%
s 47810
 
3.1%
U 46046
 
2.9%
Other values (47) 626652
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1563122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 244818
 
15.7%
i 162439
 
10.4%
n 116220
 
7.4%
e 85613
 
5.5%
l 66194
 
4.2%
r 58161
 
3.7%
o 56672
 
3.6%
p 52497
 
3.4%
s 47810
 
3.1%
U 46046
 
2.9%
Other values (47) 626652
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1563122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 244818
 
15.7%
i 162439
 
10.4%
n 116220
 
7.4%
e 85613
 
5.5%
l 66194
 
4.2%
r 58161
 
3.7%
o 56672
 
3.6%
p 52497
 
3.4%
s 47810
 
3.1%
U 46046
 
2.9%
Other values (47) 626652
40.1%

stateProvince
Text

Missing 

Distinct1219
Distinct (%)0.5%
Missing9931
Missing (%)4.2%
Memory size1.8 MiB
2025-02-14T15:18:46.453230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length30
Mean length9.678429711
Min length3

Characters and Unicode

Total characters2192716
Distinct characters66
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

Unique191 ?
Unique (%)0.1%

Sample

1st rowCoastal Prov
2nd rowYunnan
3rd rowMadre de Dios
4th rowRift Valley Prov
5th rowKigezi
ValueCountFrequency (%)
i 20030
 
5.7%
region 14466
 
4.1%
province 12114
 
3.5%
prov 11948
 
3.4%
de 11226
 
3.2%
luzon 8857
 
2.5%
california 6401
 
1.8%
western 6067
 
1.7%
el 5139
 
1.5%
beni 4996
 
1.4%
Other values (1276) 247667
71.0%
2025-02-14T15:18:46.670873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 271477
 
12.4%
n 182191
 
8.3%
o 179533
 
8.2%
i 178821
 
8.2%
e 135184
 
6.2%
r 133547
 
6.1%
122354
 
5.6%
s 83742
 
3.8%
t 79710
 
3.6%
u 75674
 
3.5%
Other values (56) 750483
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2192716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 271477
 
12.4%
n 182191
 
8.3%
o 179533
 
8.2%
i 178821
 
8.2%
e 135184
 
6.2%
r 133547
 
6.1%
122354
 
5.6%
s 83742
 
3.8%
t 79710
 
3.6%
u 75674
 
3.5%
Other values (56) 750483
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2192716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 271477
 
12.4%
n 182191
 
8.3%
o 179533
 
8.2%
i 178821
 
8.2%
e 135184
 
6.2%
r 133547
 
6.1%
122354
 
5.6%
s 83742
 
3.8%
t 79710
 
3.6%
u 75674
 
3.5%
Other values (56) 750483
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2192716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 271477
 
12.4%
n 182191
 
8.3%
o 179533
 
8.2%
i 178821
 
8.2%
e 135184
 
6.2%
r 133547
 
6.1%
122354
 
5.6%
s 83742
 
3.8%
t 79710
 
3.6%
u 75674
 
3.5%
Other values (56) 750483
34.2%

county
Text

Missing 

Distinct1677
Distinct (%)1.2%
Missing94287
Missing (%)39.9%
Memory size1.8 MiB
2025-02-14T15:18:46.820166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length23
Mean length10.91327065
Min length3

Characters and Unicode

Total characters1551878
Distinct characters75
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

Unique315 ?
Unique (%)0.2%

Sample

1st rowKwale Dist
2nd rowLijiang Pref
3rd rowManu
4th rowNakuru Dist
5th rowKisoro
ValueCountFrequency (%)
co 33919
 
13.0%
prov 20355
 
7.8%
dist 14011
 
5.4%
district 13631
 
5.2%
mamore 4298
 
1.6%
pref 4245
 
1.6%
cook 3217
 
1.2%
kenosha 2677
 
1.0%
manu 2567
 
1.0%
mindoro 2469
 
0.9%
Other values (1643) 159959
61.2%
2025-02-14T15:18:47.021589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 165403
 
10.7%
o 143128
 
9.2%
119148
 
7.7%
i 111597
 
7.2%
r 107454
 
6.9%
e 84635
 
5.5%
n 83972
 
5.4%
t 77535
 
5.0%
s 63312
 
4.1%
u 56080
 
3.6%
Other values (65) 539614
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1551878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 165403
 
10.7%
o 143128
 
9.2%
119148
 
7.7%
i 111597
 
7.2%
r 107454
 
6.9%
e 84635
 
5.5%
n 83972
 
5.4%
t 77535
 
5.0%
s 63312
 
4.1%
u 56080
 
3.6%
Other values (65) 539614
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1551878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 165403
 
10.7%
o 143128
 
9.2%
119148
 
7.7%
i 111597
 
7.2%
r 107454
 
6.9%
e 84635
 
5.5%
n 83972
 
5.4%
t 77535
 
5.0%
s 63312
 
4.1%
u 56080
 
3.6%
Other values (65) 539614
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1551878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 165403
 
10.7%
o 143128
 
9.2%
119148
 
7.7%
i 111597
 
7.2%
r 107454
 
6.9%
e 84635
 
5.5%
n 83972
 
5.4%
t 77535
 
5.0%
s 63312
 
4.1%
u 56080
 
3.6%
Other values (65) 539614
34.8%

locality
Text

Missing 

Distinct17487
Distinct (%)7.7%
Missing9170
Missing (%)3.9%
Memory size1.8 MiB
2025-02-14T15:18:47.164621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length128
Median length88
Mean length28.50562648
Min length1

Characters and Unicode

Total characters6479842
Distinct characters93
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

Unique6559 ?
Unique (%)2.9%

Sample

1st rowShimoni, "Slave Cave"
2nd rowDayan Co; Lijiang Range
3rd rowHacienda Amazonia
4th rowNaivasha, 2 mi SE; Lake Naivasha, Lake Hotel
5th rowMgahinga Gorilla Nat Pk, edge of encroached area and Pk, along pipeline
ValueCountFrequency (%)
km 71602
 
6.3%
mt 22043
 
1.9%
n 19216
 
1.7%
mi 18826
 
1.7%
e 18747
 
1.7%
w 16829
 
1.5%
forest 16235
 
1.4%
s 15297
 
1.4%
mts 14398
 
1.3%
park 14146
 
1.2%
Other values (15440) 904395
79.9%
2025-02-14T15:18:47.385577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
905128
 
14.0%
a 665885
 
10.3%
e 377341
 
5.8%
i 329585
 
5.1%
o 324433
 
5.0%
n 320239
 
4.9%
r 273558
 
4.2%
t 233601
 
3.6%
l 207301
 
3.2%
, 203713
 
3.1%
Other values (83) 2639058
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6479842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
905128
 
14.0%
a 665885
 
10.3%
e 377341
 
5.8%
i 329585
 
5.1%
o 324433
 
5.0%
n 320239
 
4.9%
r 273558
 
4.2%
t 233601
 
3.6%
l 207301
 
3.2%
, 203713
 
3.1%
Other values (83) 2639058
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6479842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
905128
 
14.0%
a 665885
 
10.3%
e 377341
 
5.8%
i 329585
 
5.1%
o 324433
 
5.0%
n 320239
 
4.9%
r 273558
 
4.2%
t 233601
 
3.6%
l 207301
 
3.2%
, 203713
 
3.1%
Other values (83) 2639058
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6479842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
905128
 
14.0%
a 665885
 
10.3%
e 377341
 
5.8%
i 329585
 
5.1%
o 324433
 
5.0%
n 320239
 
4.9%
r 273558
 
4.2%
t 233601
 
3.6%
l 207301
 
3.2%
, 203713
 
3.1%
Other values (83) 2639058
40.7%
Distinct1326
Distinct (%)1.1%
Missing116627
Missing (%)49.3%
Memory size1.8 MiB
2025-02-14T15:18:47.527829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.461618041
Min length1

Characters and Unicode

Total characters414913
Distinct characters12
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

Unique173 ?
Unique (%)0.1%

Sample

1st row7
2nd row550
3rd row2680
4th row1400
5th row320
ValueCountFrequency (%)
200 4292
 
3.6%
100 1907
 
1.6%
1000 1811
 
1.5%
50 1286
 
1.1%
10 1254
 
1.0%
570 1217
 
1.0%
1600 1216
 
1.0%
914 1113
 
0.9%
1300 1042
 
0.9%
300 1018
 
0.8%
Other values (1315) 103705
86.5%
2025-02-14T15:18:47.727072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 110787
26.7%
1 69061
16.6%
2 55077
13.3%
5 37339
 
9.0%
3 30906
 
7.4%
4 24269
 
5.8%
8 23888
 
5.8%
9 21353
 
5.1%
7 21345
 
5.1%
6 20873
 
5.0%
Other values (2) 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 414913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 110787
26.7%
1 69061
16.6%
2 55077
13.3%
5 37339
 
9.0%
3 30906
 
7.4%
4 24269
 
5.8%
8 23888
 
5.8%
9 21353
 
5.1%
7 21345
 
5.1%
6 20873
 
5.0%
Other values (2) 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 414913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 110787
26.7%
1 69061
16.6%
2 55077
13.3%
5 37339
 
9.0%
3 30906
 
7.4%
4 24269
 
5.8%
8 23888
 
5.8%
9 21353
 
5.1%
7 21345
 
5.1%
6 20873
 
5.0%
Other values (2) 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 414913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 110787
26.7%
1 69061
16.6%
2 55077
13.3%
5 37339
 
9.0%
3 30906
 
7.4%
4 24269
 
5.8%
8 23888
 
5.8%
9 21353
 
5.1%
7 21345
 
5.1%
6 20873
 
5.0%
Other values (2) 15
 
< 0.1%
Distinct132
Distinct (%)3.8%
Missing233009
Missing (%)98.5%
Memory size1.8 MiB
2025-02-14T15:18:47.809211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.554469675
Min length1

Characters and Unicode

Total characters12366
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

Unique28 ?
Unique (%)0.8%

Sample

1st row1930
2nd row1454
3rd row300
4th row2410
5th row1600
ValueCountFrequency (%)
2520 368
 
10.6%
1600 292
 
8.4%
2208 189
 
5.4%
1935 132
 
3.8%
1356 125
 
3.6%
454 120
 
3.4%
1340 101
 
2.9%
1945 100
 
2.9%
635 94
 
2.7%
2098 79
 
2.3%
Other values (122) 1879
54.0%
2025-02-14T15:18:47.942077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2588
20.9%
1 1903
15.4%
2 1841
14.9%
5 1376
11.1%
3 1087
8.8%
4 880
 
7.1%
9 790
 
6.4%
6 737
 
6.0%
8 668
 
5.4%
7 496
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2588
20.9%
1 1903
15.4%
2 1841
14.9%
5 1376
11.1%
3 1087
8.8%
4 880
 
7.1%
9 790
 
6.4%
6 737
 
6.0%
8 668
 
5.4%
7 496
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2588
20.9%
1 1903
15.4%
2 1841
14.9%
5 1376
11.1%
3 1087
8.8%
4 880
 
7.1%
9 790
 
6.4%
6 737
 
6.0%
8 668
 
5.4%
7 496
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2588
20.9%
1 1903
15.4%
2 1841
14.9%
5 1376
11.1%
3 1087
8.8%
4 880
 
7.1%
9 790
 
6.4%
6 737
 
6.0%
8 668
 
5.4%
7 496
 
4.0%

verbatimElevation
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing236487
Missing (%)> 99.9%
Memory size1.8 MiB
2025-02-14T15:18:47.976050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
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 (%)100.0%

Sample

1st row-
ValueCountFrequency (%)
1
100.0%
2025-02-14T15:18:48.052486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1
100.0%

locationRemarks
Text

Missing 

Distinct6
Distinct (%)< 0.1%
Missing94113
Missing (%)39.8%
Memory size1.8 MiB
2025-02-14T15:18:48.079425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length15
Mean length15.92458648
Min length3

Characters and Unicode

Total characters2267263
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowdecimal degrees
2nd rowdecimal degrees
3rd rowdecimal degrees
4th rowdecimal degrees
5th rowdecimal degrees
ValueCountFrequency (%)
degrees 140997
46.7%
decimal 122753
40.7%
minutes 18299
 
6.1%
seconds 18244
 
6.0%
utm 1183
 
0.4%
not 192
 
0.1%
recorded 192
 
0.1%
rijksdriehoeksmeeting 3
 
< 0.1%
2025-02-14T15:18:48.173301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 582683
25.7%
d 282381
12.5%
s 195790
 
8.6%
159488
 
7.0%
r 141384
 
6.2%
c 141189
 
6.2%
i 141061
 
6.2%
m 141055
 
6.2%
g 141000
 
6.2%
a 122753
 
5.4%
Other values (12) 218479
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2267263
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 582683
25.7%
d 282381
12.5%
s 195790
 
8.6%
159488
 
7.0%
r 141384
 
6.2%
c 141189
 
6.2%
i 141061
 
6.2%
m 141055
 
6.2%
g 141000
 
6.2%
a 122753
 
5.4%
Other values (12) 218479
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2267263
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 582683
25.7%
d 282381
12.5%
s 195790
 
8.6%
159488
 
7.0%
r 141384
 
6.2%
c 141189
 
6.2%
i 141061
 
6.2%
m 141055
 
6.2%
g 141000
 
6.2%
a 122753
 
5.4%
Other values (12) 218479
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2267263
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 582683
25.7%
d 282381
12.5%
s 195790
 
8.6%
159488
 
7.0%
r 141384
 
6.2%
c 141189
 
6.2%
i 141061
 
6.2%
m 141055
 
6.2%
g 141000
 
6.2%
a 122753
 
5.4%
Other values (12) 218479
 
9.6%

decimalLatitude
Text

Missing 

Distinct10025
Distinct (%)4.9%
Missing32326
Missing (%)13.7%
Memory size1.8 MiB
2025-02-14T15:18:48.297355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.192866449
Min length1

Characters and Unicode

Total characters1876834
Distinct characters12
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

Unique2825 ?
Unique (%)1.4%

Sample

1st row-4.21521
2nd row27.2569389
3rd row-12.9333334
4th row-0.7333333
5th row-1.3667001
ValueCountFrequency (%)
13.0666666 3663
 
1.8%
30.416666 1271
 
0.6%
3.2583301 1069
 
0.5%
42.5847206 978
 
0.5%
30.0333328 879
 
0.4%
22.3500004 725
 
0.4%
39.8083534 713
 
0.3%
5.0666666 683
 
0.3%
12.9333 638
 
0.3%
43.0372 582
 
0.3%
Other values (9572) 192961
94.5%
2025-02-14T15:18:48.516013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 267714
14.3%
6 247711
13.2%
. 201973
10.8%
0 174409
9.3%
1 169198
9.0%
9 151643
8.1%
2 135924
7.2%
4 119785
6.4%
8 112744
6.0%
5 105101
 
5.6%
Other values (2) 190632
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1876834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 267714
14.3%
6 247711
13.2%
. 201973
10.8%
0 174409
9.3%
1 169198
9.0%
9 151643
8.1%
2 135924
7.2%
4 119785
6.4%
8 112744
6.0%
5 105101
 
5.6%
Other values (2) 190632
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1876834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 267714
14.3%
6 247711
13.2%
. 201973
10.8%
0 174409
9.3%
1 169198
9.0%
9 151643
8.1%
2 135924
7.2%
4 119785
6.4%
8 112744
6.0%
5 105101
 
5.6%
Other values (2) 190632
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1876834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 267714
14.3%
6 247711
13.2%
. 201973
10.8%
0 174409
9.3%
1 169198
9.0%
9 151643
8.1%
2 135924
7.2%
4 119785
6.4%
8 112744
6.0%
5 105101
 
5.6%
Other values (2) 190632
10.2%

decimalLongitude
Text

Missing 

Distinct10195
Distinct (%)5.0%
Missing32129
Missing (%)13.6%
Memory size1.8 MiB
2025-02-14T15:18:48.659223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.880279312
Min length1

Characters and Unicode

Total characters2019124
Distinct characters12
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

Unique2939 ?
Unique (%)1.4%

Sample

1st row39.38037
2nd row100.0841522
3rd row-71.25
4th row36.4333344
5th row29.6499996
ValueCountFrequency (%)
64.8166657 3693
 
1.8%
101.6125031 1064
 
0.5%
71.25 1062
 
0.5%
87.8211746 977
 
0.5%
0.4833333 963
 
0.5%
31.1000004 731
 
0.4%
32.5666657 731
 
0.4%
120.4698334 713
 
0.3%
71.7333298 686
 
0.3%
38.5999985 626
 
0.3%
Other values (10033) 193113
94.5%
2025-02-14T15:18:48.870910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 262213
13.0%
6 237081
11.7%
1 206486
10.2%
. 202037
10.0%
9 165206
8.2%
0 161978
8.0%
8 150574
7.5%
7 150278
7.4%
2 145657
7.2%
5 131957
6.5%
Other values (2) 205657
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2019124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 262213
13.0%
6 237081
11.7%
1 206486
10.2%
. 202037
10.0%
9 165206
8.2%
0 161978
8.0%
8 150574
7.5%
7 150278
7.4%
2 145657
7.2%
5 131957
6.5%
Other values (2) 205657
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2019124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 262213
13.0%
6 237081
11.7%
1 206486
10.2%
. 202037
10.0%
9 165206
8.2%
0 161978
8.0%
8 150574
7.5%
7 150278
7.4%
2 145657
7.2%
5 131957
6.5%
Other values (2) 205657
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2019124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 262213
13.0%
6 237081
11.7%
1 206486
10.2%
. 202037
10.0%
9 165206
8.2%
0 161978
8.0%
8 150574
7.5%
7 150278
7.4%
2 145657
7.2%
5 131957
6.5%
Other values (2) 205657
10.2%

verbatimLatitude
Text

Missing 

Distinct9435
Distinct (%)4.9%
Missing44305
Missing (%)18.7%
Memory size1.8 MiB
2025-02-14T15:18:49.017961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length16
Mean length8.627516482
Min length1

Characters and Unicode

Total characters1658062
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

Unique2614 ?
Unique (%)1.4%

Sample

1st row-4.64706
2nd row27.2569
3rd row-12.9333333
4th row-0.7333333
5th row-1.3667
ValueCountFrequency (%)
s 23476
 
9.1%
n 13154
 
5.1%
13.0666667 3663
 
1.4%
1952
 
0.8%
15° 1826
 
0.7%
13° 1709
 
0.7%
12° 1597
 
0.6%
18° 1483
 
0.6%
30.4166667 1256
 
0.5%
00 1132
 
0.4%
Other values (8756) 206543
80.1%
2025-02-14T15:18:49.208908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 253259
15.3%
6 197169
11.9%
. 184882
11.2%
1 143413
8.6%
2 109539
 
6.6%
7 99741
 
6.0%
4 96655
 
5.8%
5 92348
 
5.6%
0 90406
 
5.5%
8 84539
 
5.1%
Other values (23) 306111
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1658062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 253259
15.3%
6 197169
11.9%
. 184882
11.2%
1 143413
8.6%
2 109539
 
6.6%
7 99741
 
6.0%
4 96655
 
5.8%
5 92348
 
5.6%
0 90406
 
5.5%
8 84539
 
5.1%
Other values (23) 306111
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1658062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 253259
15.3%
6 197169
11.9%
. 184882
11.2%
1 143413
8.6%
2 109539
 
6.6%
7 99741
 
6.0%
4 96655
 
5.8%
5 92348
 
5.6%
0 90406
 
5.5%
8 84539
 
5.1%
Other values (23) 306111
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1658062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 253259
15.3%
6 197169
11.9%
. 184882
11.2%
1 143413
8.6%
2 109539
 
6.6%
7 99741
 
6.0%
4 96655
 
5.8%
5 92348
 
5.6%
0 90406
 
5.5%
8 84539
 
5.1%
Other values (23) 306111
18.5%

verbatimLongitude
Text

Missing 

Distinct9690
Distinct (%)5.0%
Missing44313
Missing (%)18.7%
Memory size1.8 MiB
2025-02-14T15:18:49.338053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length16
Mean length9.29720307
Min length1

Characters and Unicode

Total characters1786690
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

Unique2797 ?
Unique (%)1.5%

Sample

1st row39.38037
2nd row100.084
3rd row-71.25
4th row36.4333333
5th row29.65
ValueCountFrequency (%)
e 29775
 
11.5%
w 7102
 
2.8%
64.8166667 3683
 
1.4%
71° 1821
 
0.7%
33° 1507
 
0.6%
47° 1507
 
0.6%
49° 1467
 
0.6%
37° 1265
 
0.5%
30 1147
 
0.4%
121° 1121
 
0.4%
Other values (9368) 207722
80.5%
2025-02-14T15:18:49.530895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 252052
14.1%
6 216692
12.1%
. 184852
10.3%
1 171836
9.6%
7 130915
 
7.3%
2 108654
 
6.1%
8 106182
 
5.9%
5 98647
 
5.5%
4 93570
 
5.2%
0 87960
 
4.9%
Other values (22) 335330
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1786690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 252052
14.1%
6 216692
12.1%
. 184852
10.3%
1 171836
9.6%
7 130915
 
7.3%
2 108654
 
6.1%
8 106182
 
5.9%
5 98647
 
5.5%
4 93570
 
5.2%
0 87960
 
4.9%
Other values (22) 335330
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1786690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 252052
14.1%
6 216692
12.1%
. 184852
10.3%
1 171836
9.6%
7 130915
 
7.3%
2 108654
 
6.1%
8 106182
 
5.9%
5 98647
 
5.5%
4 93570
 
5.2%
0 87960
 
4.9%
Other values (22) 335330
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1786690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 252052
14.1%
6 216692
12.1%
. 184852
10.3%
1 171836
9.6%
7 130915
 
7.3%
2 108654
 
6.1%
8 106182
 
5.9%
5 98647
 
5.5%
4 93570
 
5.2%
0 87960
 
4.9%
Other values (22) 335330
18.8%

georeferenceProtocol
Text

Missing 

Distinct20
Distinct (%)< 0.1%
Missing68457
Missing (%)28.9%
Memory size1.8 MiB
2025-02-14T15:18:49.567600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length25
Mean length18.24144354
Min length3

Characters and Unicode

Total characters3065128
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMaNIS georeferencing guidelines
2nd rownot recorded
3rd rownot recorded
4th rownot recorded
5th rowpublished
ValueCountFrequency (%)
not 79415
21.1%
recorded 79415
21.1%
manis 64300
17.1%
georeferencing 62908
16.7%
guidelines 62908
16.7%
gps 12620
 
3.4%
published 4806
 
1.3%
collector 4531
 
1.2%
geolocate 857
 
0.2%
wgs84 720
 
0.2%
Other values (18) 3139
 
0.8%
2025-02-14T15:18:49.655233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 550003
17.9%
r 290275
9.5%
n 268945
8.8%
o 233073
 
7.6%
d 227662
 
7.4%
207588
 
6.8%
i 194867
 
6.4%
g 189625
 
6.2%
c 152017
 
5.0%
t 86461
 
2.8%
Other values (31) 664612
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3065128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 550003
17.9%
r 290275
9.5%
n 268945
8.8%
o 233073
 
7.6%
d 227662
 
7.4%
207588
 
6.8%
i 194867
 
6.4%
g 189625
 
6.2%
c 152017
 
5.0%
t 86461
 
2.8%
Other values (31) 664612
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3065128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 550003
17.9%
r 290275
9.5%
n 268945
8.8%
o 233073
 
7.6%
d 227662
 
7.4%
207588
 
6.8%
i 194867
 
6.4%
g 189625
 
6.2%
c 152017
 
5.0%
t 86461
 
2.8%
Other values (31) 664612
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3065128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 550003
17.9%
r 290275
9.5%
n 268945
8.8%
o 233073
 
7.6%
d 227662
 
7.4%
207588
 
6.8%
i 194867
 
6.4%
g 189625
 
6.2%
c 152017
 
5.0%
t 86461
 
2.8%
Other values (31) 664612
21.7%

typeStatus
Text

Missing 

Distinct4
Distinct (%)0.7%
Missing235877
Missing (%)99.7%
Memory size1.8 MiB
2025-02-14T15:18:49.684631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.980360065
Min length7

Characters and Unicode

Total characters4876
Distinct characters11
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 rowHolotype
2nd rowparatype
3rd rowHolotype
4th rowHolotype
5th rowHolotype
ValueCountFrequency (%)
holotype 536
87.7%
paratype 63
 
10.3%
neotype 12
 
2.0%
2025-02-14T15:18:49.767699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1084
22.2%
p 645
13.2%
e 623
12.8%
t 611
12.5%
y 611
12.5%
H 536
11.0%
l 536
11.0%
a 126
 
2.6%
r 63
 
1.3%
P 29
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1084
22.2%
p 645
13.2%
e 623
12.8%
t 611
12.5%
y 611
12.5%
H 536
11.0%
l 536
11.0%
a 126
 
2.6%
r 63
 
1.3%
P 29
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1084
22.2%
p 645
13.2%
e 623
12.8%
t 611
12.5%
y 611
12.5%
H 536
11.0%
l 536
11.0%
a 126
 
2.6%
r 63
 
1.3%
P 29
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1084
22.2%
p 645
13.2%
e 623
12.8%
t 611
12.5%
y 611
12.5%
H 536
11.0%
l 536
11.0%
a 126
 
2.6%
r 63
 
1.3%
P 29
 
0.6%

identifiedBy
Text

Missing 

Distinct27
Distinct (%)1.4%
Missing234549
Missing (%)99.2%
Memory size1.8 MiB
2025-02-14T15:18:49.806709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length71
Median length66
Mean length56.90923156
Min length12

Characters and Unicode

Total characters110347
Distinct characters51
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

Unique12 ?
Unique (%)0.6%

Sample

1st rowDr. Julian C. Kerbis : Field Museum of Natural History
2nd rowDr. Bruce D. Patterson : Field Museum of Natural History - Mammals
3rd rowDr. Lawrence R. Heaney : Field Museum of Natural History - Mammals
4th rowDr. Bruce D. Patterson : Field Museum of Natural History - Mammals
5th rowDr. Lawrence R. Heaney : Field Museum of Natural History - Mammals
ValueCountFrequency (%)
2774
13.7%
of 1790
 
8.8%
museum 1785
 
8.8%
natural 1785
 
8.8%
history 1785
 
8.8%
dr 1728
 
8.5%
field 1702
 
8.4%
mammals 962
 
4.7%
d 739
 
3.6%
c 673
 
3.3%
Other values (83) 4557
22.5%
2025-02-14T15:18:49.910691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18341
16.6%
a 7897
 
7.2%
r 7804
 
7.1%
e 7327
 
6.6%
u 6767
 
6.1%
s 6003
 
5.4%
l 5330
 
4.8%
t 5052
 
4.6%
i 4996
 
4.5%
o 4412
 
4.0%
Other values (41) 36418
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18341
16.6%
a 7897
 
7.2%
r 7804
 
7.1%
e 7327
 
6.6%
u 6767
 
6.1%
s 6003
 
5.4%
l 5330
 
4.8%
t 5052
 
4.6%
i 4996
 
4.5%
o 4412
 
4.0%
Other values (41) 36418
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18341
16.6%
a 7897
 
7.2%
r 7804
 
7.1%
e 7327
 
6.6%
u 6767
 
6.1%
s 6003
 
5.4%
l 5330
 
4.8%
t 5052
 
4.6%
i 4996
 
4.5%
o 4412
 
4.0%
Other values (41) 36418
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18341
16.6%
a 7897
 
7.2%
r 7804
 
7.1%
e 7327
 
6.6%
u 6767
 
6.1%
s 6003
 
5.4%
l 5330
 
4.8%
t 5052
 
4.6%
i 4996
 
4.5%
o 4412
 
4.0%
Other values (41) 36418
33.0%

dateIdentified
Text

Missing 

Distinct14
Distinct (%)0.4%
Missing233049
Missing (%)98.5%
Memory size1.8 MiB
2025-02-14T15:18:49.943640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters13756
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row2016
2nd row2016
3rd row2019
4th row2016
5th row2019
ValueCountFrequency (%)
2010 1043
30.3%
2019 779
22.7%
2016 375
 
10.9%
2018 337
 
9.8%
2020 248
 
7.2%
2011 230
 
6.7%
2017 208
 
6.0%
2009 198
 
5.8%
2015 11
 
0.3%
2005 4
 
0.1%
Other values (4) 6
 
0.2%
2025-02-14T15:18:50.029044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4934
35.9%
2 3686
26.8%
1 3217
23.4%
9 978
 
7.1%
6 376
 
2.7%
8 338
 
2.5%
7 209
 
1.5%
5 15
 
0.1%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4934
35.9%
2 3686
26.8%
1 3217
23.4%
9 978
 
7.1%
6 376
 
2.7%
8 338
 
2.5%
7 209
 
1.5%
5 15
 
0.1%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4934
35.9%
2 3686
26.8%
1 3217
23.4%
9 978
 
7.1%
6 376
 
2.7%
8 338
 
2.5%
7 209
 
1.5%
5 15
 
0.1%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4934
35.9%
2 3686
26.8%
1 3217
23.4%
9 978
 
7.1%
6 376
 
2.7%
8 338
 
2.5%
7 209
 
1.5%
5 15
 
0.1%
4 3
 
< 0.1%
Distinct8059
Distinct (%)3.4%
Missing61
Missing (%)< 0.1%
Memory size1.8 MiB
2025-02-14T15:18:50.144095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length37
Mean length22.06037382
Min length3

Characters and Unicode

Total characters5215668
Distinct characters63
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

Unique1401 ?
Unique (%)0.6%

Sample

1st rowRhinolophus landeri
2nd rowEothenomys melanogaster fidelis
3rd rowCarollia brevicauda
4th rowChaerephon pumilus
5th rowLophuromys woosnami
ValueCountFrequency (%)
rattus 11545
 
2.0%
peromyscus 9362
 
1.6%
praomys 8045
 
1.4%
crocidura 7613
 
1.3%
lophuromys 5877
 
1.0%
maniculatus 4971
 
0.9%
myotis 4947
 
0.9%
rhinolophus 4735
 
0.8%
mus 4686
 
0.8%
abrothrix 4577
 
0.8%
Other values (5592) 505338
88.4%
2025-02-14T15:18:50.418130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 546173
 
10.5%
i 464524
 
8.9%
a 417230
 
8.0%
o 371192
 
7.1%
u 365822
 
7.0%
335269
 
6.4%
r 326821
 
6.3%
e 310209
 
5.9%
n 247763
 
4.8%
l 238621
 
4.6%
Other values (53) 1592044
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5215668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 546173
 
10.5%
i 464524
 
8.9%
a 417230
 
8.0%
o 371192
 
7.1%
u 365822
 
7.0%
335269
 
6.4%
r 326821
 
6.3%
e 310209
 
5.9%
n 247763
 
4.8%
l 238621
 
4.6%
Other values (53) 1592044
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5215668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 546173
 
10.5%
i 464524
 
8.9%
a 417230
 
8.0%
o 371192
 
7.1%
u 365822
 
7.0%
335269
 
6.4%
r 326821
 
6.3%
e 310209
 
5.9%
n 247763
 
4.8%
l 238621
 
4.6%
Other values (53) 1592044
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5215668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 546173
 
10.5%
i 464524
 
8.9%
a 417230
 
8.0%
o 371192
 
7.1%
u 365822
 
7.0%
335269
 
6.4%
r 326821
 
6.3%
e 310209
 
5.9%
n 247763
 
4.8%
l 238621
 
4.6%
Other values (53) 1592044
30.5%
Distinct161
Distinct (%)0.1%
Missing61
Missing (%)< 0.1%
Memory size1.8 MiB
2025-02-14T15:18:50.469268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length57
Mean length47.15661917
Min length26

Characters and Unicode

Total characters11149098
Distinct characters43
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia Chordata Mammalia Chiroptera Rhinolophidae
2nd rowAnimalia Chordata Mammalia Rodentia Cricetidae
3rd rowAnimalia Chordata Mammalia Chiroptera Phyllostomidae
4th rowAnimalia Chordata Mammalia Chiroptera Molossidae
5th rowAnimalia Chordata Mammalia Rodentia Muridae
ValueCountFrequency (%)
animalia 236427
20.0%
mammalia 236427
20.0%
chordata 236427
20.0%
rodentia 123236
10.4%
chiroptera 63786
 
5.4%
muridae 56910
 
4.8%
cricetidae 41848
 
3.5%
vespertilionidae 15997
 
1.4%
soricomorpha 14742
 
1.2%
phyllostomidae 14077
 
1.2%
Other values (168) 141512
12.0%
2025-02-14T15:18:50.575432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2166564
19.4%
i 1316689
11.8%
944962
 
8.5%
m 762022
 
6.8%
o 639728
 
5.7%
d 628408
 
5.6%
r 620941
 
5.6%
e 562667
 
5.0%
l 558796
 
5.0%
t 540516
 
4.8%
Other values (33) 2407805
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11149098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2166564
19.4%
i 1316689
11.8%
944962
 
8.5%
m 762022
 
6.8%
o 639728
 
5.7%
d 628408
 
5.6%
r 620941
 
5.6%
e 562667
 
5.0%
l 558796
 
5.0%
t 540516
 
4.8%
Other values (33) 2407805
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11149098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2166564
19.4%
i 1316689
11.8%
944962
 
8.5%
m 762022
 
6.8%
o 639728
 
5.7%
d 628408
 
5.6%
r 620941
 
5.6%
e 562667
 
5.0%
l 558796
 
5.0%
t 540516
 
4.8%
Other values (33) 2407805
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11149098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2166564
19.4%
i 1316689
11.8%
944962
 
8.5%
m 762022
 
6.8%
o 639728
 
5.7%
d 628408
 
5.6%
r 620941
 
5.6%
e 562667
 
5.0%
l 558796
 
5.0%
t 540516
 
4.8%
Other values (33) 2407805
21.6%

kingdom
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing61
Missing (%)< 0.1%
Memory size1.8 MiB
2025-02-14T15:18:50.605102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1891416
Distinct characters6
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 rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 236427
100.0%
2025-02-14T15:18:50.681014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 472854
25.0%
a 472854
25.0%
A 236427
12.5%
n 236427
12.5%
m 236427
12.5%
l 236427
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 472854
25.0%
a 472854
25.0%
A 236427
12.5%
n 236427
12.5%
m 236427
12.5%
l 236427
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 472854
25.0%
a 472854
25.0%
A 236427
12.5%
n 236427
12.5%
m 236427
12.5%
l 236427
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 472854
25.0%
a 472854
25.0%
A 236427
12.5%
n 236427
12.5%
m 236427
12.5%
l 236427
12.5%

phylum
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing61
Missing (%)< 0.1%
Memory size1.8 MiB
2025-02-14T15:18:50.709325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1891416
Distinct characters7
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 rowChordata
2nd rowChordata
3rd rowChordata
4th rowChordata
5th rowChordata
ValueCountFrequency (%)
chordata 236427
100.0%
2025-02-14T15:18:50.785195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 472854
25.0%
C 236427
12.5%
h 236427
12.5%
o 236427
12.5%
r 236427
12.5%
d 236427
12.5%
t 236427
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 472854
25.0%
C 236427
12.5%
h 236427
12.5%
o 236427
12.5%
r 236427
12.5%
d 236427
12.5%
t 236427
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 472854
25.0%
C 236427
12.5%
h 236427
12.5%
o 236427
12.5%
r 236427
12.5%
d 236427
12.5%
t 236427
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 472854
25.0%
C 236427
12.5%
h 236427
12.5%
o 236427
12.5%
r 236427
12.5%
d 236427
12.5%
t 236427
12.5%

class
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing61
Missing (%)< 0.1%
Memory size1.8 MiB
2025-02-14T15:18:50.813890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1891416
Distinct characters5
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 rowMammalia
2nd rowMammalia
3rd rowMammalia
4th rowMammalia
5th rowMammalia
ValueCountFrequency (%)
mammalia 236427
100.0%
2025-02-14T15:18:50.885111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 709281
37.5%
m 472854
25.0%
M 236427
 
12.5%
l 236427
 
12.5%
i 236427
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 709281
37.5%
m 472854
25.0%
M 236427
 
12.5%
l 236427
 
12.5%
i 236427
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 709281
37.5%
m 472854
25.0%
M 236427
 
12.5%
l 236427
 
12.5%
i 236427
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1891416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 709281
37.5%
m 472854
25.0%
M 236427
 
12.5%
l 236427
 
12.5%
i 236427
 
12.5%

order
Text

Distinct29
Distinct (%)< 0.1%
Missing742
Missing (%)0.3%
Memory size1.8 MiB
2025-02-14T15:18:50.913850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length8
Mean length9.127064722
Min length6

Characters and Unicode

Total characters2151669
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 rowChiroptera
2nd rowRodentia
3rd rowChiroptera
4th rowChiroptera
5th rowRodentia
ValueCountFrequency (%)
rodentia 123236
52.3%
chiroptera 63786
27.1%
soricomorpha 14742
 
6.3%
carnivora 12164
 
5.2%
primates 5271
 
2.2%
artiodactyla 4611
 
2.0%
afrosoricida 3137
 
1.3%
didelphimorphia 2039
 
0.9%
lagomorpha 1896
 
0.8%
erinaceomorpha 867
 
0.4%
Other values (19) 3997
 
1.7%
2025-02-14T15:18:51.009143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 266189
12.4%
a 258168
12.0%
i 241567
11.2%
r 205994
9.6%
t 205278
9.5%
e 198354
9.2%
n 138633
6.4%
d 135413
6.3%
R 123236
5.7%
p 86373
 
4.0%
Other values (22) 292464
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2151669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 266189
12.4%
a 258168
12.0%
i 241567
11.2%
r 205994
9.6%
t 205278
9.5%
e 198354
9.2%
n 138633
6.4%
d 135413
6.3%
R 123236
5.7%
p 86373
 
4.0%
Other values (22) 292464
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2151669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 266189
12.4%
a 258168
12.0%
i 241567
11.2%
r 205994
9.6%
t 205278
9.5%
e 198354
9.2%
n 138633
6.4%
d 135413
6.3%
R 123236
5.7%
p 86373
 
4.0%
Other values (22) 292464
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2151669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 266189
12.4%
a 258168
12.0%
i 241567
11.2%
r 205994
9.6%
t 205278
9.5%
e 198354
9.2%
n 138633
6.4%
d 135413
6.3%
R 123236
5.7%
p 86373
 
4.0%
Other values (22) 292464
13.6%

family
Text

Distinct146
Distinct (%)0.1%
Missing126
Missing (%)0.1%
Memory size1.8 MiB
2025-02-14T15:18:51.045864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length17
Mean length10.06176543
Min length6

Characters and Unicode

Total characters2378219
Distinct characters42
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 (%)< 0.1%

Sample

1st rowRhinolophidae
2nd rowCricetidae
3rd rowPhyllostomidae
4th rowMolossidae
5th rowMuridae
ValueCountFrequency (%)
muridae 56910
24.1%
cricetidae 41848
17.7%
vespertilionidae 15997
 
6.8%
phyllostomidae 14077
 
6.0%
soricidae 14065
 
6.0%
sciuridae 11759
 
5.0%
pteropodidae 9384
 
4.0%
molossidae 8266
 
3.5%
mustelidae 4867
 
2.1%
hipposideridae 4828
 
2.0%
Other values (136) 54361
23.0%
2025-02-14T15:18:51.143474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 365841
15.4%
e 364313
15.3%
d 256568
10.8%
a 253407
10.7%
r 178520
 
7.5%
o 137112
 
5.8%
t 98811
 
4.2%
c 83300
 
3.5%
u 78620
 
3.3%
l 77501
 
3.3%
Other values (32) 484226
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2378219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 365841
15.4%
e 364313
15.3%
d 256568
10.8%
a 253407
10.7%
r 178520
 
7.5%
o 137112
 
5.8%
t 98811
 
4.2%
c 83300
 
3.5%
u 78620
 
3.3%
l 77501
 
3.3%
Other values (32) 484226
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2378219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 365841
15.4%
e 364313
15.3%
d 256568
10.8%
a 253407
10.7%
r 178520
 
7.5%
o 137112
 
5.8%
t 98811
 
4.2%
c 83300
 
3.5%
u 78620
 
3.3%
l 77501
 
3.3%
Other values (32) 484226
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2378219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 365841
15.4%
e 364313
15.3%
d 256568
10.8%
a 253407
10.7%
r 178520
 
7.5%
o 137112
 
5.8%
t 98811
 
4.2%
c 83300
 
3.5%
u 78620
 
3.3%
l 77501
 
3.3%
Other values (32) 484226
20.4%

genus
Text

Distinct1050
Distinct (%)0.4%
Missing215
Missing (%)0.1%
Memory size1.8 MiB
2025-02-14T15:18:51.275117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.566759638
Min length2

Characters and Unicode

Total characters2024094
Distinct characters50
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

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowRhinolophus
2nd rowEothenomys
3rd rowCarollia
4th rowChaerephon
5th rowLophuromys
ValueCountFrequency (%)
peromyscus 9362
 
4.0%
praomys 8045
 
3.4%
rattus 7641
 
3.2%
crocidura 7613
 
3.2%
lophuromys 5877
 
2.5%
myotis 4793
 
2.0%
rhinolophus 4735
 
2.0%
mus 4683
 
2.0%
abrothrix 4577
 
1.9%
apomys 4015
 
1.7%
Other values (1040) 174932
74.0%
2025-02-14T15:18:51.484441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 226904
 
11.2%
s 224014
 
11.1%
r 150018
 
7.4%
i 131718
 
6.5%
u 127542
 
6.3%
a 115421
 
5.7%
e 108848
 
5.4%
y 101258
 
5.0%
m 94959
 
4.7%
t 89402
 
4.4%
Other values (40) 654010
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2024094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 226904
 
11.2%
s 224014
 
11.1%
r 150018
 
7.4%
i 131718
 
6.5%
u 127542
 
6.3%
a 115421
 
5.7%
e 108848
 
5.4%
y 101258
 
5.0%
m 94959
 
4.7%
t 89402
 
4.4%
Other values (40) 654010
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2024094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 226904
 
11.2%
s 224014
 
11.1%
r 150018
 
7.4%
i 131718
 
6.5%
u 127542
 
6.3%
a 115421
 
5.7%
e 108848
 
5.4%
y 101258
 
5.0%
m 94959
 
4.7%
t 89402
 
4.4%
Other values (40) 654010
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2024094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 226904
 
11.2%
s 224014
 
11.1%
r 150018
 
7.4%
i 131718
 
6.5%
u 127542
 
6.3%
a 115421
 
5.7%
e 108848
 
5.4%
y 101258
 
5.0%
m 94959
 
4.7%
t 89402
 
4.4%
Other values (40) 654010
32.3%

subgenus
Text

Missing 

Distinct147
Distinct (%)0.7%
Missing216134
Missing (%)91.4%
Memory size1.8 MiB
2025-02-14T15:18:51.592932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.563771249
Min length3

Characters and Unicode

Total characters174307
Distinct characters44
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

Unique13 ?
Unique (%)0.1%

Sample

1st rowKivumys
2nd rowKivumys
3rd rowTaterona
4th rowLophuromys
5th rowPipistrellus
ValueCountFrequency (%)
lophuromys 4302
21.1%
mops 1530
 
7.5%
nannomys 1512
 
7.4%
akodon 1101
 
5.4%
kivumys 993
 
4.9%
artibeus 914
 
4.5%
sturnira 670
 
3.3%
corvira 640
 
3.1%
pallasiomys 563
 
2.8%
rousettus 505
 
2.5%
Other values (137) 7624
37.5%
2025-02-14T15:18:51.753420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 20824
 
11.9%
s 18968
 
10.9%
r 13505
 
7.7%
u 13209
 
7.6%
y 10032
 
5.8%
m 9939
 
5.7%
a 9417
 
5.4%
i 8713
 
5.0%
p 7755
 
4.4%
e 7523
 
4.3%
Other values (34) 54422
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 174307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 20824
 
11.9%
s 18968
 
10.9%
r 13505
 
7.7%
u 13209
 
7.6%
y 10032
 
5.8%
m 9939
 
5.7%
a 9417
 
5.4%
i 8713
 
5.0%
p 7755
 
4.4%
e 7523
 
4.3%
Other values (34) 54422
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 174307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 20824
 
11.9%
s 18968
 
10.9%
r 13505
 
7.7%
u 13209
 
7.6%
y 10032
 
5.8%
m 9939
 
5.7%
a 9417
 
5.4%
i 8713
 
5.0%
p 7755
 
4.4%
e 7523
 
4.3%
Other values (34) 54422
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 174307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 20824
 
11.9%
s 18968
 
10.9%
r 13505
 
7.7%
u 13209
 
7.6%
y 10032
 
5.8%
m 9939
 
5.7%
a 9417
 
5.4%
i 8713
 
5.0%
p 7755
 
4.4%
e 7523
 
4.3%
Other values (34) 54422
31.2%

specificEpithet
Text

Missing 

Distinct2590
Distinct (%)1.1%
Missing9549
Missing (%)4.0%
Memory size1.8 MiB
2025-02-14T15:18:51.886331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length15
Mean length8.43733338
Min length2

Characters and Unicode

Total characters1914760
Distinct characters27
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

Unique259 ?
Unique (%)0.1%

Sample

1st rowlanderi
2nd rowmelanogaster
3rd rowbrevicauda
4th rowpumilus
5th rowwoosnami
ValueCountFrequency (%)
maniculatus 4624
 
2.0%
aquilus 3923
 
1.7%
jacksoni 3494
 
1.5%
olivacea 2925
 
1.3%
vison 2811
 
1.2%
rattus 2490
 
1.1%
musculus 2465
 
1.1%
taitae 2018
 
0.9%
callosus 1909
 
0.8%
natalensis 1888
 
0.8%
Other values (2581) 198395
87.4%
2025-02-14T15:18:52.084381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 218537
11.4%
s 211903
11.1%
a 206775
10.8%
u 166747
 
8.7%
e 129298
 
6.8%
n 124550
 
6.5%
l 115519
 
6.0%
r 113756
 
5.9%
o 99792
 
5.2%
t 95942
 
5.0%
Other values (17) 431941
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1914760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 218537
11.4%
s 211903
11.1%
a 206775
10.8%
u 166747
 
8.7%
e 129298
 
6.8%
n 124550
 
6.5%
l 115519
 
6.0%
r 113756
 
5.9%
o 99792
 
5.2%
t 95942
 
5.0%
Other values (17) 431941
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1914760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 218537
11.4%
s 211903
11.1%
a 206775
10.8%
u 166747
 
8.7%
e 129298
 
6.8%
n 124550
 
6.5%
l 115519
 
6.0%
r 113756
 
5.9%
o 99792
 
5.2%
t 95942
 
5.0%
Other values (17) 431941
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1914760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 218537
11.4%
s 211903
11.1%
a 206775
10.8%
u 166747
 
8.7%
e 129298
 
6.8%
n 124550
 
6.5%
l 115519
 
6.0%
r 113756
 
5.9%
o 99792
 
5.2%
t 95942
 
5.0%
Other values (17) 431941
22.6%