Overview

Brought to you by YData

Dataset statistics

Number of variables70
Number of observations724508
Missing cells30334160
Missing cells (%)59.8%
Total size in memory386.9 MiB
Average record size in memory560.0 B

Variable types

Text70

Dataset

DescriptionNMNH Paleobiology Specimen Records (USNM) 0049391-241126133413365
URLhttps://doi.org/10.15468/7m0fvd

Alerts

institutionID has constant value "http://biocol.org/urn:lsid:biocol.org:col:34871" Constant
collectionID has constant value "urn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac" Constant
institutionCode has constant value "USNM" Constant
collectionCode has constant value "PAL" Constant
datasetName has constant value "NMNH Paleobiology (USNM)" Constant
basisOfRecord has constant value "FossilSpecimen" Constant
verbatimCoordinateSystem has constant value "Degrees Minutes Seconds" Constant
catalogNumber has 50535 (7.0%) missing values Missing
recordNumber has 675939 (93.3%) missing values Missing
recordedBy has 563497 (77.8%) missing values Missing
preparations has 591600 (81.7%) missing values Missing
associatedMedia has 637195 (87.9%) missing values Missing
occurrenceRemarks has 638259 (88.1%) missing values Missing
fieldNumber has 720044 (99.4%) missing values Missing
eventDate has 453741 (62.6%) missing values Missing
startDayOfYear has 571939 (78.9%) missing values Missing
endDayOfYear has 571953 (78.9%) missing values Missing
year has 453741 (62.6%) missing values Missing
month has 571556 (78.9%) missing values Missing
day has 593848 (82.0%) missing values Missing
verbatimEventDate has 445814 (61.5%) missing values Missing
locationID has 335037 (46.2%) missing values Missing
higherGeography has 148417 (20.5%) missing values Missing
continent has 210428 (29.0%) missing values Missing
waterBody has 696851 (96.2%) missing values Missing
islandGroup has 723710 (99.9%) missing values Missing
island has 714401 (98.6%) missing values Missing
country has 173269 (23.9%) missing values Missing
stateProvince has 226462 (31.3%) missing values Missing
county has 454433 (62.7%) missing values Missing
locality has 560871 (77.4%) missing values Missing
verbatimElevation has 724311 (> 99.9%) missing values Missing
verbatimDepth has 724424 (> 99.9%) missing values Missing
decimalLatitude has 620569 (85.7%) missing values Missing
decimalLongitude has 620569 (85.7%) missing values Missing
geodeticDatum has 698201 (96.4%) missing values Missing
verbatimLatitude has 724503 (> 99.9%) missing values Missing
verbatimLongitude has 724503 (> 99.9%) missing values Missing
verbatimCoordinateSystem has 654265 (90.3%) missing values Missing
georeferenceProtocol has 695012 (95.9%) missing values Missing
georeferenceRemarks has 724503 (> 99.9%) missing values Missing
earliestEraOrLowestErathem has 220036 (30.4%) missing values Missing
latestEraOrHighestErathem has 718163 (99.1%) missing values Missing
earliestPeriodOrLowestSystem has 245750 (33.9%) missing values Missing
latestPeriodOrHighestSystem has 718167 (99.1%) missing values Missing
earliestEpochOrLowestSeries has 376914 (52.0%) missing values Missing
latestEpochOrHighestSeries has 718290 (99.1%) missing values Missing
earliestAgeOrLowestStage has 562472 (77.6%) missing values Missing
latestAgeOrHighestStage has 722133 (99.7%) missing values Missing
group has 633218 (87.4%) missing values Missing
formation has 365706 (50.5%) missing values Missing
member has 643191 (88.8%) missing values Missing
typeStatus has 581882 (80.3%) missing values Missing
identifiedBy has 521981 (72.0%) missing values Missing
scientificName has 171332 (23.6%) missing values Missing
higherClassification has 172643 (23.8%) missing values Missing
kingdom has 172847 (23.9%) missing values Missing
phylum has 211856 (29.2%) missing values Missing
class has 235611 (32.5%) missing values Missing
order has 400004 (55.2%) missing values Missing
family has 409455 (56.5%) missing values Missing
genus has 197061 (27.2%) missing values Missing
subgenus has 702202 (96.9%) missing values Missing
specificEpithet has 197674 (27.3%) missing values Missing
infraspecificEpithet has 708037 (97.7%) missing values Missing
taxonRank has 707802 (97.7%) missing values Missing
scientificNameAuthorship has 325030 (44.9%) missing values Missing
gbifID has unique values Unique
occurrenceID has unique values Unique

Reproduction

Analysis started2025-02-10 18:46:34.862003
Analysis finished2025-02-10 18:46:52.766926
Duration17.9 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct724508
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:53.179062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7245080
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

Unique724508 ?
Unique (%)100.0%

Sample

1st row1316557253
2nd row2235727162
3rd row1316557263
4th row1316557258
5th row1316557269
ValueCountFrequency (%)
1316557253 1
 
< 0.1%
1316557860 1
 
< 0.1%
1316557419 1
 
< 0.1%
1316557667 1
 
< 0.1%
1316557340 1
 
< 0.1%
1316557263 1
 
< 0.1%
1316557258 1
 
< 0.1%
1316557269 1
 
< 0.1%
1316557294 1
 
< 0.1%
3311036301 1
 
< 0.1%
Other values (724498) 724498
> 99.9%
2025-02-10T13:46:53.694617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1858630
25.7%
3 1114337
15.4%
6 924334
12.8%
7 682226
 
9.4%
0 507951
 
7.0%
8 482636
 
6.7%
9 467327
 
6.5%
5 426943
 
5.9%
2 401616
 
5.5%
4 379080
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7245080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1858630
25.7%
3 1114337
15.4%
6 924334
12.8%
7 682226
 
9.4%
0 507951
 
7.0%
8 482636
 
6.7%
9 467327
 
6.5%
5 426943
 
5.9%
2 401616
 
5.5%
4 379080
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7245080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1858630
25.7%
3 1114337
15.4%
6 924334
12.8%
7 682226
 
9.4%
0 507951
 
7.0%
8 482636
 
6.7%
9 467327
 
6.5%
5 426943
 
5.9%
2 401616
 
5.5%
4 379080
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7245080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1858630
25.7%
3 1114337
15.4%
6 924334
12.8%
7 682226
 
9.4%
0 507951
 
7.0%
8 482636
 
6.7%
9 467327
 
6.5%
5 426943
 
5.9%
2 401616
 
5.5%
4 379080
 
5.2%
Distinct6008
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:53.745304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters13765652
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

Unique1783 ?
Unique (%)0.2%

Sample

1st row2014-11-25 18:32:00
2nd row2024-10-17 09:58:00
3rd row2024-10-17 10:44:00
4th row2024-08-03 21:41:00
5th row2024-10-17 10:17:00
ValueCountFrequency (%)
2024-10-17 379839
26.2%
2024-08-03 110663
 
7.6%
2014-12-01 62342
 
4.3%
2014-11-25 62169
 
4.3%
2024-11-18 18663
 
1.3%
2014-11-26 16425
 
1.1%
2022-07-29 12130
 
0.8%
22:06:00 11127
 
0.8%
11:08:00 10895
 
0.8%
22:09:00 9244
 
0.6%
Other values (1703) 755519
52.1%
2025-02-10T13:46:53.833795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3567224
25.9%
1 2229486
16.2%
2 1840704
13.4%
- 1449016
10.5%
: 1449016
10.5%
4 856419
 
6.2%
724508
 
5.3%
7 523431
 
3.8%
3 323301
 
2.3%
8 267407
 
1.9%
Other values (3) 535140
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13765652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3567224
25.9%
1 2229486
16.2%
2 1840704
13.4%
- 1449016
10.5%
: 1449016
10.5%
4 856419
 
6.2%
724508
 
5.3%
7 523431
 
3.8%
3 323301
 
2.3%
8 267407
 
1.9%
Other values (3) 535140
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13765652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3567224
25.9%
1 2229486
16.2%
2 1840704
13.4%
- 1449016
10.5%
: 1449016
10.5%
4 856419
 
6.2%
724508
 
5.3%
7 523431
 
3.8%
3 323301
 
2.3%
8 267407
 
1.9%
Other values (3) 535140
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13765652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3567224
25.9%
1 2229486
16.2%
2 1840704
13.4%
- 1449016
10.5%
: 1449016
10.5%
4 856419
 
6.2%
724508
 
5.3%
7 523431
 
3.8%
3 323301
 
2.3%
8 267407
 
1.9%
Other values (3) 535140
 
3.9%

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:53.863519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length47
Median length47
Mean length47
Min length47

Characters and Unicode

Total characters34051876
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 rowhttp://biocol.org/urn:lsid:biocol.org:col:34871
2nd rowhttp://biocol.org/urn:lsid:biocol.org:col:34871
3rd rowhttp://biocol.org/urn:lsid:biocol.org:col:34871
4th rowhttp://biocol.org/urn:lsid:biocol.org:col:34871
5th rowhttp://biocol.org/urn:lsid:biocol.org:col:34871
ValueCountFrequency (%)
http://biocol.org/urn:lsid:biocol.org:col:34871 724508
100.0%
2025-02-10T13:46:53.948134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5071556
14.9%
: 3622540
 
10.6%
l 2898032
 
8.5%
r 2173524
 
6.4%
/ 2173524
 
6.4%
i 2173524
 
6.4%
c 2173524
 
6.4%
b 1449016
 
4.3%
. 1449016
 
4.3%
t 1449016
 
4.3%
Other values (12) 9418604
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34051876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5071556
14.9%
: 3622540
 
10.6%
l 2898032
 
8.5%
r 2173524
 
6.4%
/ 2173524
 
6.4%
i 2173524
 
6.4%
c 2173524
 
6.4%
b 1449016
 
4.3%
. 1449016
 
4.3%
t 1449016
 
4.3%
Other values (12) 9418604
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34051876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5071556
14.9%
: 3622540
 
10.6%
l 2898032
 
8.5%
r 2173524
 
6.4%
/ 2173524
 
6.4%
i 2173524
 
6.4%
c 2173524
 
6.4%
b 1449016
 
4.3%
. 1449016
 
4.3%
t 1449016
 
4.3%
Other values (12) 9418604
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34051876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5071556
14.9%
: 3622540
 
10.6%
l 2898032
 
8.5%
r 2173524
 
6.4%
/ 2173524
 
6.4%
i 2173524
 
6.4%
c 2173524
 
6.4%
b 1449016
 
4.3%
. 1449016
 
4.3%
t 1449016
 
4.3%
Other values (12) 9418604
27.7%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:53.978856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters31878352
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 rowurn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac
2nd rowurn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac
3rd rowurn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac
4th rowurn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac
5th rowurn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac
ValueCountFrequency (%)
urn:uuid:ce595e88-ceba-42c0-a3ff-cd55b694fac 724508
100.0%
2025-02-10T13:46:54.071460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 3622540
 
11.4%
- 2898032
 
9.1%
5 2898032
 
9.1%
u 2173524
 
6.8%
f 2173524
 
6.8%
a 2173524
 
6.8%
e 2173524
 
6.8%
4 1449016
 
4.5%
b 1449016
 
4.5%
8 1449016
 
4.5%
Other values (10) 9418604
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31878352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 3622540
 
11.4%
- 2898032
 
9.1%
5 2898032
 
9.1%
u 2173524
 
6.8%
f 2173524
 
6.8%
a 2173524
 
6.8%
e 2173524
 
6.8%
4 1449016
 
4.5%
b 1449016
 
4.5%
8 1449016
 
4.5%
Other values (10) 9418604
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31878352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 3622540
 
11.4%
- 2898032
 
9.1%
5 2898032
 
9.1%
u 2173524
 
6.8%
f 2173524
 
6.8%
a 2173524
 
6.8%
e 2173524
 
6.8%
4 1449016
 
4.5%
b 1449016
 
4.5%
8 1449016
 
4.5%
Other values (10) 9418604
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31878352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 3622540
 
11.4%
- 2898032
 
9.1%
5 2898032
 
9.1%
u 2173524
 
6.8%
f 2173524
 
6.8%
a 2173524
 
6.8%
e 2173524
 
6.8%
4 1449016
 
4.5%
b 1449016
 
4.5%
8 1449016
 
4.5%
Other values (10) 9418604
29.5%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:54.106051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2898032
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 rowUSNM
2nd rowUSNM
3rd rowUSNM
4th rowUSNM
5th rowUSNM
ValueCountFrequency (%)
usnm 724508
100.0%
2025-02-10T13:46:54.187567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 724508
25.0%
S 724508
25.0%
N 724508
25.0%
M 724508
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2898032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 724508
25.0%
S 724508
25.0%
N 724508
25.0%
M 724508
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2898032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 724508
25.0%
S 724508
25.0%
N 724508
25.0%
M 724508
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2898032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 724508
25.0%
S 724508
25.0%
N 724508
25.0%
M 724508
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:54.217494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2173524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAL
2nd rowPAL
3rd rowPAL
4th rowPAL
5th rowPAL
ValueCountFrequency (%)
pal 724508
100.0%
2025-02-10T13:46:54.298836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 724508
33.3%
A 724508
33.3%
L 724508
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2173524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 724508
33.3%
A 724508
33.3%
L 724508
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2173524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 724508
33.3%
A 724508
33.3%
L 724508
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2173524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 724508
33.3%
A 724508
33.3%
L 724508
33.3%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:54.329381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters17388192
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 rowNMNH Paleobiology (USNM)
2nd rowNMNH Paleobiology (USNM)
3rd rowNMNH Paleobiology (USNM)
4th rowNMNH Paleobiology (USNM)
5th rowNMNH Paleobiology (USNM)
ValueCountFrequency (%)
nmnh 724508
33.3%
paleobiology 724508
33.3%
usnm 724508
33.3%
2025-02-10T13:46:54.412225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2173524
12.5%
o 2173524
12.5%
1449016
 
8.3%
l 1449016
 
8.3%
M 1449016
 
8.3%
H 724508
 
4.2%
P 724508
 
4.2%
a 724508
 
4.2%
e 724508
 
4.2%
b 724508
 
4.2%
Other values (7) 5071556
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17388192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2173524
12.5%
o 2173524
12.5%
1449016
 
8.3%
l 1449016
 
8.3%
M 1449016
 
8.3%
H 724508
 
4.2%
P 724508
 
4.2%
a 724508
 
4.2%
e 724508
 
4.2%
b 724508
 
4.2%
Other values (7) 5071556
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17388192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2173524
12.5%
o 2173524
12.5%
1449016
 
8.3%
l 1449016
 
8.3%
M 1449016
 
8.3%
H 724508
 
4.2%
P 724508
 
4.2%
a 724508
 
4.2%
e 724508
 
4.2%
b 724508
 
4.2%
Other values (7) 5071556
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17388192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2173524
12.5%
o 2173524
12.5%
1449016
 
8.3%
l 1449016
 
8.3%
M 1449016
 
8.3%
H 724508
 
4.2%
P 724508
 
4.2%
a 724508
 
4.2%
e 724508
 
4.2%
b 724508
 
4.2%
Other values (7) 5071556
29.2%

basisOfRecord
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:54.440920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters10143112
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 rowFossilSpecimen
2nd rowFossilSpecimen
3rd rowFossilSpecimen
4th rowFossilSpecimen
5th rowFossilSpecimen
ValueCountFrequency (%)
fossilspecimen 724508
100.0%
2025-02-10T13:46:54.523833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 1449016
14.3%
i 1449016
14.3%
e 1449016
14.3%
F 724508
7.1%
o 724508
7.1%
l 724508
7.1%
S 724508
7.1%
p 724508
7.1%
c 724508
7.1%
m 724508
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10143112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1449016
14.3%
i 1449016
14.3%
e 1449016
14.3%
F 724508
7.1%
o 724508
7.1%
l 724508
7.1%
S 724508
7.1%
p 724508
7.1%
c 724508
7.1%
m 724508
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10143112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1449016
14.3%
i 1449016
14.3%
e 1449016
14.3%
F 724508
7.1%
o 724508
7.1%
l 724508
7.1%
S 724508
7.1%
p 724508
7.1%
c 724508
7.1%
m 724508
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10143112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1449016
14.3%
i 1449016
14.3%
e 1449016
14.3%
F 724508
7.1%
o 724508
7.1%
l 724508
7.1%
S 724508
7.1%
p 724508
7.1%
c 724508
7.1%
m 724508
7.1%

occurrenceID
Text

Unique 

Distinct724508
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-02-10T13:46:54.842211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters45644004
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique724508 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/300009e1e-4f3e-4240-b198-9ea1352b28b5
2nd rowhttp://n2t.net/ark:/65665/30000a59d-34e5-42b6-837d-ad1b89b6b930
3rd rowhttp://n2t.net/ark:/65665/3000109b9-b6d6-4ca0-8f0c-ddde53458300
4th rowhttp://n2t.net/ark:/65665/30001bcd8-61d5-492a-ad56-f8131f24bdaa
5th rowhttp://n2t.net/ark:/65665/300020a6b-970f-4e44-adb4-6d605be80b0d
ValueCountFrequency (%)
http://n2t.net/ark:/65665/300009e1e-4f3e-4240-b198-9ea1352b28b5 1
 
< 0.1%
http://n2t.net/ark:/65665/3004266bd-f222-4227-9817-5905ac4cbc57 1
 
< 0.1%
http://n2t.net/ark:/65665/30011b937-0eb9-4c75-bea7-c27393598b76 1
 
< 0.1%
http://n2t.net/ark:/65665/3002cb891-3b1b-49d8-84ee-8558aba9bf13 1
 
< 0.1%
http://n2t.net/ark:/65665/3000a6387-0469-4278-8ac0-fb0ac6fd37d6 1
 
< 0.1%
http://n2t.net/ark:/65665/3000109b9-b6d6-4ca0-8f0c-ddde53458300 1
 
< 0.1%
http://n2t.net/ark:/65665/30001bcd8-61d5-492a-ad56-f8131f24bdaa 1
 
< 0.1%
http://n2t.net/ark:/65665/300020a6b-970f-4e44-adb4-6d605be80b0d 1
 
< 0.1%
http://n2t.net/ark:/65665/300045523-2307-4a34-b888-fb51510870ad 1
 
< 0.1%
http://n2t.net/ark:/65665/300045db2-681e-481a-836e-3643bf3debbf 1
 
< 0.1%
Other values (724498) 724498
> 99.9%
2025-02-10T13:46:55.232242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 3622540
 
7.9%
6 3531516
 
7.7%
- 2898032
 
6.3%
t 2898032
 
6.3%
5 2808306
 
6.2%
a 2263386
 
5.0%
e 2084462
 
4.6%
2 2083197
 
4.6%
3 2083153
 
4.6%
4 2081137
 
4.6%
Other values (16) 19290243
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45644004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 3622540
 
7.9%
6 3531516
 
7.7%
- 2898032
 
6.3%
t 2898032
 
6.3%
5 2808306
 
6.2%
a 2263386
 
5.0%
e 2084462
 
4.6%
2 2083197
 
4.6%
3 2083153
 
4.6%
4 2081137
 
4.6%
Other values (16) 19290243
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45644004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 3622540
 
7.9%
6 3531516
 
7.7%
- 2898032
 
6.3%
t 2898032
 
6.3%
5 2808306
 
6.2%
a 2263386
 
5.0%
e 2084462
 
4.6%
2 2083197
 
4.6%
3 2083153
 
4.6%
4 2081137
 
4.6%
Other values (16) 19290243
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45644004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 3622540
 
7.9%
6 3531516
 
7.7%
- 2898032
 
6.3%
t 2898032
 
6.3%
5 2808306
 
6.2%
a 2263386
 
5.0%
e 2084462
 
4.6%
2 2083197
 
4.6%
3 2083153
 
4.6%
4 2081137
 
4.6%
Other values (16) 19290243
42.3%

catalogNumber
Text

Missing 

Distinct655081
Distinct (%)97.2%
Missing50535
Missing (%)7.0%
Memory size5.5 MiB
2025-02-10T13:46:55.522628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length14
Mean length13.86868317
Min length7

Characters and Unicode

Total characters9347118
Distinct characters68
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

Unique638257 ?
Unique (%)94.7%

Sample

1st rowUSNM SD38013 0000
2nd rowUSNM PAL706968
3rd rowUSNM PAL248638
4th rowUSNM PAL456768
5th rowUSNM PAL297724
ValueCountFrequency (%)
usnm 673973
47.8%
0000 59177
 
4.2%
0002 159
 
< 0.1%
0001 159
 
< 0.1%
0003 149
 
< 0.1%
0004 145
 
< 0.1%
0005 137
 
< 0.1%
0006 116
 
< 0.1%
0007 113
 
< 0.1%
0008 105
 
< 0.1%
Other values (652937) 674632
47.9%
2025-02-10T13:46:55.862356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 742844
 
7.9%
734892
 
7.9%
M 712585
 
7.6%
N 674519
 
7.2%
U 674214
 
7.2%
0 557394
 
6.0%
P 521957
 
5.6%
A 511374
 
5.5%
L 497601
 
5.3%
1 444334
 
4.8%
Other values (58) 3275404
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9347118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 742844
 
7.9%
734892
 
7.9%
M 712585
 
7.6%
N 674519
 
7.2%
U 674214
 
7.2%
0 557394
 
6.0%
P 521957
 
5.6%
A 511374
 
5.5%
L 497601
 
5.3%
1 444334
 
4.8%
Other values (58) 3275404
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9347118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 742844
 
7.9%
734892
 
7.9%
M 712585
 
7.6%
N 674519
 
7.2%
U 674214
 
7.2%
0 557394
 
6.0%
P 521957
 
5.6%
A 511374
 
5.5%
L 497601
 
5.3%
1 444334
 
4.8%
Other values (58) 3275404
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9347118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 742844
 
7.9%
734892
 
7.9%
M 712585
 
7.6%
N 674519
 
7.2%
U 674214
 
7.2%
0 557394
 
6.0%
P 521957
 
5.6%
A 511374
 
5.5%
L 497601
 
5.3%
1 444334
 
4.8%
Other values (58) 3275404
35.0%

recordNumber
Text

Missing 

Distinct39872
Distinct (%)82.1%
Missing675939
Missing (%)93.3%
Memory size5.5 MiB
2025-02-10T13:46:56.015722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length5
Mean length6.205336737
Min length1

Characters and Unicode

Total characters301387
Distinct characters77
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

Unique37721 ?
Unique (%)77.7%

Sample

1st rowPALMER LOC 1479
2nd row75432
3rd rowH-11
4th rowE73-59
5th rowGaxin Loc 178-36
ValueCountFrequency (%)
loc 1685
 
2.9%
emlong 951
 
1.7%
urbac 803
 
1.4%
olson 263
 
0.5%
sample 209
 
0.4%
hass 177
 
0.3%
rb 171
 
0.3%
c-29 169
 
0.3%
gibson 163
 
0.3%
wyo 162
 
0.3%
Other values (38506) 52476
91.7%
2025-02-10T13:46:56.235910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30021
 
10.0%
5 27939
 
9.3%
7 23690
 
7.9%
2 21570
 
7.2%
3 20657
 
6.9%
6 18998
 
6.3%
8 18791
 
6.2%
0 17388
 
5.8%
4 17006
 
5.6%
- 16559
 
5.5%
Other values (67) 88768
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 30021
 
10.0%
5 27939
 
9.3%
7 23690
 
7.9%
2 21570
 
7.2%
3 20657
 
6.9%
6 18998
 
6.3%
8 18791
 
6.2%
0 17388
 
5.8%
4 17006
 
5.6%
- 16559
 
5.5%
Other values (67) 88768
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 30021
 
10.0%
5 27939
 
9.3%
7 23690
 
7.9%
2 21570
 
7.2%
3 20657
 
6.9%
6 18998
 
6.3%
8 18791
 
6.2%
0 17388
 
5.8%
4 17006
 
5.6%
- 16559
 
5.5%
Other values (67) 88768
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 30021
 
10.0%
5 27939
 
9.3%
7 23690
 
7.9%
2 21570
 
7.2%
3 20657
 
6.9%
6 18998
 
6.3%
8 18791
 
6.2%
0 17388
 
5.8%
4 17006
 
5.6%
- 16559
 
5.5%
Other values (67) 88768
29.5%

recordedBy
Text

Missing 

Distinct3957
Distinct (%)2.5%
Missing563497
Missing (%)77.8%
Memory size5.5 MiB
2025-02-10T13:46:56.385779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length119
Median length61
Mean length10.93147052
Min length1

Characters and Unicode

Total characters1760087
Distinct characters61
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

Unique1329 ?
Unique (%)0.8%

Sample

1st rowR. Snow
2nd rowD. Palmer
3rd rowW. Woodring & L. Lupher
4th rowJames
5th rowRoss
ValueCountFrequency (%)
21228
 
6.1%
j 19727
 
5.7%
r 15376
 
4.5%
w 14249
 
4.1%
a 12060
 
3.5%
james 11468
 
3.3%
l 10757
 
3.1%
woodring 9356
 
2.7%
pribyl 8943
 
2.6%
c 7362
 
2.1%
Other values (2560) 214833
62.2%
2025-02-10T13:46:56.611593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184348
 
10.5%
e 133592
 
7.6%
. 131492
 
7.5%
r 102132
 
5.8%
o 91217
 
5.2%
l 89319
 
5.1%
n 89079
 
5.1%
a 84651
 
4.8%
i 80231
 
4.6%
s 70452
 
4.0%
Other values (51) 703574
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1760087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
184348
 
10.5%
e 133592
 
7.6%
. 131492
 
7.5%
r 102132
 
5.8%
o 91217
 
5.2%
l 89319
 
5.1%
n 89079
 
5.1%
a 84651
 
4.8%
i 80231
 
4.6%
s 70452
 
4.0%
Other values (51) 703574
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1760087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
184348
 
10.5%
e 133592
 
7.6%
. 131492
 
7.5%
r 102132
 
5.8%
o 91217
 
5.2%
l 89319
 
5.1%
n 89079
 
5.1%
a 84651
 
4.8%
i 80231
 
4.6%
s 70452
 
4.0%
Other values (51) 703574
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1760087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
184348
 
10.5%
e 133592
 
7.6%
. 131492
 
7.5%
r 102132
 
5.8%
o 91217
 
5.2%
l 89319
 
5.1%
n 89079
 
5.1%
a 84651
 
4.8%
i 80231
 
4.6%
s 70452
 
4.0%
Other values (51) 703574
40.0%
Distinct686
Distinct (%)0.1%
Missing303
Missing (%)< 0.1%
Memory size5.5 MiB
2025-02-10T13:46:56.645962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.088909908
Min length1

Characters and Unicode

Total characters788594
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

Unique253 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row25
5th row1
ValueCountFrequency (%)
1 594864
82.1%
2 29629
 
4.1%
3 14673
 
2.0%
4 9858
 
1.4%
5 7420
 
1.0%
6 5780
 
0.8%
7 4510
 
0.6%
8 3695
 
0.5%
10 3151
 
0.4%
9 3129
 
0.4%
Other values (676) 47496
 
6.6%
2025-02-10T13:46:56.746989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 624602
79.2%
2 43921
 
5.6%
0 28217
 
3.6%
3 23988
 
3.0%
5 17293
 
2.2%
4 17104
 
2.2%
6 10762
 
1.4%
7 9146
 
1.2%
8 7494
 
1.0%
9 6067
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 788594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 624602
79.2%
2 43921
 
5.6%
0 28217
 
3.6%
3 23988
 
3.0%
5 17293
 
2.2%
4 17104
 
2.2%
6 10762
 
1.4%
7 9146
 
1.2%
8 7494
 
1.0%
9 6067
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 788594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 624602
79.2%
2 43921
 
5.6%
0 28217
 
3.6%
3 23988
 
3.0%
5 17293
 
2.2%
4 17104
 
2.2%
6 10762
 
1.4%
7 9146
 
1.2%
8 7494
 
1.0%
9 6067
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 788594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 624602
79.2%
2 43921
 
5.6%
0 28217
 
3.6%
3 23988
 
3.0%
5 17293
 
2.2%
4 17104
 
2.2%
6 10762
 
1.4%
7 9146
 
1.2%
8 7494
 
1.0%
9 6067
 
0.8%

preparations
Text

Missing 

Distinct381
Distinct (%)0.3%
Missing591600
Missing (%)81.7%
Memory size5.5 MiB
2025-02-10T13:46:56.792241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length94
Median length91
Mean length16.14684594
Min length3

Characters and Unicode

Total characters2146045
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

Unique130 ?
Unique (%)0.1%

Sample

1st rowBoxes and vials
2nd rowThin sections
3rd rowSecondary microslides
4th rowWet
5th rowplastic container
ValueCountFrequency (%)
microslide 45697
17.5%
microslides 34837
13.4%
secondary 33230
12.8%
remnants 26629
10.2%
thin 24547
9.4%
sections 24011
9.2%
no 15071
 
5.8%
with 10919
 
4.2%
unsectioned 9109
 
3.5%
bottle 3934
 
1.5%
Other values (53) 32636
12.5%
2025-02-10T13:46:56.907032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 236706
11.0%
s 211809
9.9%
e 210870
9.8%
n 172401
 
8.0%
o 167894
 
7.8%
c 147453
 
6.9%
r 146905
 
6.8%
d 130804
 
6.1%
127712
 
6.0%
l 92477
 
4.3%
Other values (41) 501014
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2146045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 236706
11.0%
s 211809
9.9%
e 210870
9.8%
n 172401
 
8.0%
o 167894
 
7.8%
c 147453
 
6.9%
r 146905
 
6.8%
d 130804
 
6.1%
127712
 
6.0%
l 92477
 
4.3%
Other values (41) 501014
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2146045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 236706
11.0%
s 211809
9.9%
e 210870
9.8%
n 172401
 
8.0%
o 167894
 
7.8%
c 147453
 
6.9%
r 146905
 
6.8%
d 130804
 
6.1%
127712
 
6.0%
l 92477
 
4.3%
Other values (41) 501014
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2146045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 236706
11.0%
s 211809
9.9%
e 210870
9.8%
n 172401
 
8.0%
o 167894
 
7.8%
c 147453
 
6.9%
r 146905
 
6.8%
d 130804
 
6.1%
127712
 
6.0%
l 92477
 
4.3%
Other values (41) 501014
23.3%

associatedMedia
Text

Missing 

Distinct84848
Distinct (%)97.2%
Missing637195
Missing (%)87.9%
Memory size5.5 MiB
2025-02-10T13:46:57.047119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1069
Median length1059
Mean length58.46043544
Min length48

Characters and Unicode

Total characters5104356
Distinct characters31
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

Unique83728 ?
Unique (%)95.9%

Sample

1st rowhttps://collections.nmnh.si.edu/media/?i=12688993
2nd rowhttps://collections.nmnh.si.edu/media/?i=12689748
3rd rowhttps://collections.nmnh.si.edu/media/?i=15308925
4th rowhttps://collections.nmnh.si.edu/media/?i=11098487
5th rowhttps://collections.nmnh.si.edu/media/?i=12770417; 12770964
ValueCountFrequency (%)
https://collections.nmnh.si.edu/media/?i=16189563 203
 
0.1%
https://collections.nmnh.si.edu/media/?i=16053361 170
 
0.1%
10035032 87
 
0.1%
https://collections.nmnh.si.edu/media/?i=13958963 76
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16647294 48
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16725276 37
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16115280 33
 
< 0.1%
10320533 30
 
< 0.1%
10320530 29
 
< 0.1%
10320532 26
 
< 0.1%
Other values (167678) 170293
99.6%
2025-02-10T13:46:57.266393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 349252
 
6.8%
/ 349252
 
6.8%
n 261939
 
5.1%
s 261939
 
5.1%
t 261939
 
5.1%
. 261939
 
5.1%
e 261939
 
5.1%
1 256693
 
5.0%
d 174626
 
3.4%
m 174626
 
3.4%
Other values (21) 2490212
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5104356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 349252
 
6.8%
/ 349252
 
6.8%
n 261939
 
5.1%
s 261939
 
5.1%
t 261939
 
5.1%
. 261939
 
5.1%
e 261939
 
5.1%
1 256693
 
5.0%
d 174626
 
3.4%
m 174626
 
3.4%
Other values (21) 2490212
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5104356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 349252
 
6.8%
/ 349252
 
6.8%
n 261939
 
5.1%
s 261939
 
5.1%
t 261939
 
5.1%
. 261939
 
5.1%
e 261939
 
5.1%
1 256693
 
5.0%
d 174626
 
3.4%
m 174626
 
3.4%
Other values (21) 2490212
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5104356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 349252
 
6.8%
/ 349252
 
6.8%
n 261939
 
5.1%
s 261939
 
5.1%
t 261939
 
5.1%
. 261939
 
5.1%
e 261939
 
5.1%
1 256693
 
5.0%
d 174626
 
3.4%
m 174626
 
3.4%
Other values (21) 2490212
48.8%

occurrenceRemarks
Text

Missing 

Distinct38195
Distinct (%)44.3%
Missing638259
Missing (%)88.1%
Memory size5.5 MiB
2025-02-10T13:46:57.361661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1257
Median length1240
Mean length357.4557966
Min length5

Characters and Unicode

Total characters30830205
Distinct characters92
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

Unique36384 ?
Unique (%)42.2%

Sample

1st rowSpecimen comments: Associated w/ #0343 and #0346. | Body size code: medium; Taphonomic Significance: Human modification | Features: Weathering, diagenesis: N/A; Burn Color: none; Burn Modification: none; Cut: 0; Scrape: 0; Chop: 0; Loading Notch: 0; Counterblow: 0; Anvil pit: 0; Carn pit: 0; Carn score: 0; Carn furrow: 0; Carn punct: 0; Carn crenulation: 0; Rodent gnaw: none
2nd rowEMu record was created as part of the Smithsonian Institution Digitization Program Office (SI DPO) mass digitization pilot project to support the National Science Foundation Advancing Digitization of Biodiversity Collections Eastern Pacific Invertebrates of the Cenozoic Collaborative Thematic Collections Network (NSF ADBC EPICC TCN). The SI DPO mass digitization pilot workflow includes crowdsourced label transcription through the SI Transcription Center.; Information generated by NMNH Department of Paleobiology volunteers: Specimen count and preliminary identification to class.
3rd rowEMu record was created as part of the Smithsonian Institution Digitization Program Office (SI DPO) mass digitization pilot project to support the National Science Foundation Advancing Digitization of Biodiversity Collections Eastern Pacific Invertebrates of the Cenozoic Collaborative Thematic Collections Network (NSF ADBC EPICC TCN). The SI DPO mass digitization pilot workflow includes crowdsourced label transcription through the SI Transcription Center.; Information generated by NMNH Department of Paleobiology volunteers: Specimen count and preliminary identification to class.
4th rowThe fossil is marked with the original Green River number and is often mistaken for the USNM number. That original Green River collection number is 75432.; Numbers associated with this fossil: 578683. 75432. 40193.
5th rowEMu record was created as part of the Smithsonian Institution Digitization Program Office (SI DPO) mass digitization pilot project to support the National Science Foundation Advancing Digitization of Biodiversity Collections Eastern Pacific Invertebrates of the Cenozoic Collaborative Thematic Collections Network (NSF ADBC EPICC TCN). The SI DPO mass digitization pilot workflow includes crowdsourced label transcription through the SI Transcription Center.; Additional label information: This locality is at approximately the same horizon as USGS CENO LOC 5686, in which a shale fauna was collected | See USGS CENO LOC 5703; Verbatim Lithostratigraphy: Tejon Formation; Sandstone forming the upper member of the Tejon | Discontinuous lenses in a soft brownish sandstone, less than 100 feet stratigraphically below the overlying diatomaceous shale; Verbatim Chronostratigraphy: Eocene
ValueCountFrequency (%)
the 291111
 
6.9%
digitization 174338
 
4.1%
of 164357
 
3.9%
si 100203
 
2.4%
collections 99405
 
2.4%
number 86263
 
2.0%
is 85833
 
2.0%
mass 74949
 
1.8%
dpo 74947
 
1.8%
with 57325
 
1.4%
Other values (66970) 3009589
71.3%
2025-02-10T13:46:57.521191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4132071
 
13.4%
i 2608470
 
8.5%
t 2311910
 
7.5%
o 2139574
 
6.9%
e 2129723
 
6.9%
n 1708168
 
5.5%
a 1671073
 
5.4%
r 1554155
 
5.0%
s 1249854
 
4.1%
c 981043
 
3.2%
Other values (82) 10344164
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30830205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4132071
 
13.4%
i 2608470
 
8.5%
t 2311910
 
7.5%
o 2139574
 
6.9%
e 2129723
 
6.9%
n 1708168
 
5.5%
a 1671073
 
5.4%
r 1554155
 
5.0%
s 1249854
 
4.1%
c 981043
 
3.2%
Other values (82) 10344164
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30830205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4132071
 
13.4%
i 2608470
 
8.5%
t 2311910
 
7.5%
o 2139574
 
6.9%
e 2129723
 
6.9%
n 1708168
 
5.5%
a 1671073
 
5.4%
r 1554155
 
5.0%
s 1249854
 
4.1%
c 981043
 
3.2%
Other values (82) 10344164
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30830205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4132071
 
13.4%
i 2608470
 
8.5%
t 2311910
 
7.5%
o 2139574
 
6.9%
e 2129723
 
6.9%
n 1708168
 
5.5%
a 1671073
 
5.4%
r 1554155
 
5.0%
s 1249854
 
4.1%
c 981043
 
3.2%
Other values (82) 10344164
33.6%

fieldNumber
Text

Missing 

Distinct1516
Distinct (%)34.0%
Missing720044
Missing (%)99.4%
Memory size5.5 MiB
2025-02-10T13:46:57.671690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length209
Median length45
Mean length35.25537634
Min length1

Characters and Unicode

Total characters157380
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

Unique1229 ?
Unique (%)27.5%

Sample

1st rowMTC-08009; MTC-08009B; MTC-08009B (A); MTC-08009B (B)
2nd row217
3rd rowYP79-2
4th rowTDP31
5th row82-10; 82-19; 82-21; 82-22; 82-4; 82-6; 82-7
ValueCountFrequency (%)
82-10 767
 
4.2%
82-21 767
 
4.2%
82-22 767
 
4.2%
82-4 767
 
4.2%
82-6 767
 
4.2%
82-7 767
 
4.2%
82-19 767
 
4.2%
mtc-04028dd 329
 
1.8%
mtc-04028h 329
 
1.8%
mtc-04028gg 329
 
1.8%
Other values (1502) 11759
64.9%
2025-02-10T13:46:57.885369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18832
12.0%
- 15944
10.1%
2 14513
9.2%
13651
 
8.7%
; 12694
 
8.1%
8 11928
 
7.6%
C 9870
 
6.3%
M 9201
 
5.8%
T 8674
 
5.5%
4 7381
 
4.7%
Other values (62) 34692
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18832
12.0%
- 15944
10.1%
2 14513
9.2%
13651
 
8.7%
; 12694
 
8.1%
8 11928
 
7.6%
C 9870
 
6.3%
M 9201
 
5.8%
T 8674
 
5.5%
4 7381
 
4.7%
Other values (62) 34692
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18832
12.0%
- 15944
10.1%
2 14513
9.2%
13651
 
8.7%
; 12694
 
8.1%
8 11928
 
7.6%
C 9870
 
6.3%
M 9201
 
5.8%
T 8674
 
5.5%
4 7381
 
4.7%
Other values (62) 34692
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18832
12.0%
- 15944
10.1%
2 14513
9.2%
13651
 
8.7%
; 12694
 
8.1%
8 11928
 
7.6%
C 9870
 
6.3%
M 9201
 
5.8%
T 8674
 
5.5%
4 7381
 
4.7%
Other values (62) 34692
22.0%

eventDate
Text

Missing 

Distinct17617
Distinct (%)6.5%
Missing453741
Missing (%)62.6%
Memory size5.5 MiB
2025-02-10T13:46:58.041439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length7.649425521
Min length4

Characters and Unicode

Total characters2071212
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

Unique5897 ?
Unique (%)2.2%

Sample

1st row1985-01-23
2nd row1974
3rd row1980
4th row1963
5th row1956
ValueCountFrequency (%)
1910/1917 6616
 
2.4%
1991/1993 6310
 
2.3%
1999 3773
 
1.4%
1980 3739
 
1.4%
1982 3572
 
1.3%
1984-02 3350
 
1.2%
1998 3319
 
1.2%
1997 3308
 
1.2%
1995 3121
 
1.2%
2001 2926
 
1.1%
Other values (17607) 230733
85.2%
2025-02-10T13:46:58.261763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 451090
21.8%
9 375304
18.1%
- 289583
14.0%
0 255834
12.4%
8 133815
 
6.5%
7 127284
 
6.1%
2 109700
 
5.3%
6 89305
 
4.3%
3 74141
 
3.6%
4 71285
 
3.4%
Other values (3) 93871
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2071212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 451090
21.8%
9 375304
18.1%
- 289583
14.0%
0 255834
12.4%
8 133815
 
6.5%
7 127284
 
6.1%
2 109700
 
5.3%
6 89305
 
4.3%
3 74141
 
3.6%
4 71285
 
3.4%
Other values (3) 93871
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2071212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 451090
21.8%
9 375304
18.1%
- 289583
14.0%
0 255834
12.4%
8 133815
 
6.5%
7 127284
 
6.1%
2 109700
 
5.3%
6 89305
 
4.3%
3 74141
 
3.6%
4 71285
 
3.4%
Other values (3) 93871
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2071212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 451090
21.8%
9 375304
18.1%
- 289583
14.0%
0 255834
12.4%
8 133815
 
6.5%
7 127284
 
6.1%
2 109700
 
5.3%
6 89305
 
4.3%
3 74141
 
3.6%
4 71285
 
3.4%
Other values (3) 93871
 
4.5%

startDayOfYear
Text

Missing 

Distinct366
Distinct (%)0.2%
Missing571939
Missing (%)78.9%
Memory size5.5 MiB
2025-02-10T13:46:58.415410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.836395336
Min length1

Characters and Unicode

Total characters432746
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 row23
2nd row267
3rd row230
4th row288
5th row100
ValueCountFrequency (%)
60 3645
 
2.4%
212 3066
 
2.0%
243 2888
 
1.9%
181 2290
 
1.5%
151 2068
 
1.4%
304 1900
 
1.2%
213 1765
 
1.2%
120 1640
 
1.1%
273 1383
 
0.9%
244 1217
 
0.8%
Other values (356) 130707
85.7%
2025-02-10T13:46:58.713303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 95911
22.2%
1 86225
19.9%
3 48550
11.2%
0 34306
 
7.9%
4 30194
 
7.0%
9 29540
 
6.8%
6 28135
 
6.5%
5 27414
 
6.3%
8 26265
 
6.1%
7 26206
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 95911
22.2%
1 86225
19.9%
3 48550
11.2%
0 34306
 
7.9%
4 30194
 
7.0%
9 29540
 
6.8%
6 28135
 
6.5%
5 27414
 
6.3%
8 26265
 
6.1%
7 26206
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 95911
22.2%
1 86225
19.9%
3 48550
11.2%
0 34306
 
7.9%
4 30194
 
7.0%
9 29540
 
6.8%
6 28135
 
6.5%
5 27414
 
6.3%
8 26265
 
6.1%
7 26206
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 95911
22.2%
1 86225
19.9%
3 48550
11.2%
0 34306
 
7.9%
4 30194
 
7.0%
9 29540
 
6.8%
6 28135
 
6.5%
5 27414
 
6.3%
8 26265
 
6.1%
7 26206
 
6.1%

endDayOfYear
Text

Missing 

Distinct366
Distinct (%)0.2%
Missing571953
Missing (%)78.9%
Memory size5.5 MiB
2025-02-10T13:46:58.860039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.837606109
Min length1

Characters and Unicode

Total characters432891
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 row23
2nd row267
3rd row230
4th row288
5th row100
ValueCountFrequency (%)
60 3687
 
2.4%
243 3058
 
2.0%
212 2958
 
1.9%
151 2041
 
1.3%
181 2016
 
1.3%
304 1825
 
1.2%
120 1813
 
1.2%
213 1760
 
1.2%
273 1430
 
0.9%
244 1424
 
0.9%
Other values (356) 130543
85.6%
2025-02-10T13:46:59.072401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 96077
22.2%
1 85473
19.7%
3 48226
11.1%
0 34296
 
7.9%
4 30948
 
7.1%
9 29109
 
6.7%
6 28569
 
6.6%
5 27645
 
6.4%
7 26568
 
6.1%
8 25980
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 96077
22.2%
1 85473
19.7%
3 48226
11.1%
0 34296
 
7.9%
4 30948
 
7.1%
9 29109
 
6.7%
6 28569
 
6.6%
5 27645
 
6.4%
7 26568
 
6.1%
8 25980
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 96077
22.2%
1 85473
19.7%
3 48226
11.1%
0 34296
 
7.9%
4 30948
 
7.1%
9 29109
 
6.7%
6 28569
 
6.6%
5 27645
 
6.4%
7 26568
 
6.1%
8 25980
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 96077
22.2%
1 85473
19.7%
3 48226
11.1%
0 34296
 
7.9%
4 30948
 
7.1%
9 29109
 
6.7%
6 28569
 
6.6%
5 27645
 
6.4%
7 26568
 
6.1%
8 25980
 
6.0%

year
Text

Missing 

Distinct191
Distinct (%)0.1%
Missing453741
Missing (%)62.6%
Memory size5.5 MiB
2025-02-10T13:46:59.192550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1083068
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

Unique11 ?
Unique (%)< 0.1%

Sample

1st row1985
2nd row1974
3rd row1980
4th row1963
5th row1956
ValueCountFrequency (%)
1910 7846
 
2.9%
1991 7769
 
2.9%
1980 7431
 
2.7%
1981 7192
 
2.7%
1982 7174
 
2.6%
1971 6769
 
2.5%
1976 6488
 
2.4%
1964 5815
 
2.1%
1973 5778
 
2.1%
1984 5612
 
2.1%
Other values (181) 202893
74.9%
2025-02-10T13:46:59.362815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 322145
29.7%
9 319500
29.5%
8 89146
 
8.2%
7 77505
 
7.2%
6 58473
 
5.4%
0 54161
 
5.0%
4 44737
 
4.1%
5 40639
 
3.8%
2 38510
 
3.6%
3 38252
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1083068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 322145
29.7%
9 319500
29.5%
8 89146
 
8.2%
7 77505
 
7.2%
6 58473
 
5.4%
0 54161
 
5.0%
4 44737
 
4.1%
5 40639
 
3.8%
2 38510
 
3.6%
3 38252
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1083068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 322145
29.7%
9 319500
29.5%
8 89146
 
8.2%
7 77505
 
7.2%
6 58473
 
5.4%
0 54161
 
5.0%
4 44737
 
4.1%
5 40639
 
3.8%
2 38510
 
3.6%
3 38252
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1083068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 322145
29.7%
9 319500
29.5%
8 89146
 
8.2%
7 77505
 
7.2%
6 58473
 
5.4%
0 54161
 
5.0%
4 44737
 
4.1%
5 40639
 
3.8%
2 38510
 
3.6%
3 38252
 
3.5%

month
Text

Missing 

Distinct12
Distinct (%)< 0.1%
Missing571556
Missing (%)78.9%
Memory size5.5 MiB
2025-02-10T13:46:59.402001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.158729536
Min length1

Characters and Unicode

Total characters177230
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 row1
2nd row9
3rd row8
4th row10
5th row4
ValueCountFrequency (%)
8 25708
16.8%
7 25619
16.7%
6 15211
9.9%
5 14666
9.6%
10 14523
9.5%
9 14275
9.3%
4 11358
7.4%
2 8535
 
5.6%
3 8472
 
5.5%
11 6678
 
4.4%
Other values (2) 7907
 
5.2%
2025-02-10T13:46:59.492570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 35786
20.2%
8 25708
14.5%
7 25619
14.5%
6 15211
8.6%
5 14666
8.3%
0 14523
8.2%
9 14275
 
8.1%
2 11612
 
6.6%
4 11358
 
6.4%
3 8472
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 35786
20.2%
8 25708
14.5%
7 25619
14.5%
6 15211
8.6%
5 14666
8.3%
0 14523
8.2%
9 14275
 
8.1%
2 11612
 
6.6%
4 11358
 
6.4%
3 8472
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 35786
20.2%
8 25708
14.5%
7 25619
14.5%
6 15211
8.6%
5 14666
8.3%
0 14523
8.2%
9 14275
 
8.1%
2 11612
 
6.6%
4 11358
 
6.4%
3 8472
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 35786
20.2%
8 25708
14.5%
7 25619
14.5%
6 15211
8.6%
5 14666
8.3%
0 14523
8.2%
9 14275
 
8.1%
2 11612
 
6.6%
4 11358
 
6.4%
3 8472
 
4.8%

day
Text

Missing 

Distinct31
Distinct (%)< 0.1%
Missing593848
Missing (%)82.0%
Memory size5.5 MiB
2025-02-10T13:46:59.540284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.719868361
Min length1

Characters and Unicode

Total characters224718
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 row23
2nd row24
3rd row18
4th row14
5th row9
ValueCountFrequency (%)
17 5517
 
4.2%
16 5029
 
3.8%
18 5015
 
3.8%
13 4668
 
3.6%
23 4653
 
3.6%
14 4622
 
3.5%
20 4591
 
3.5%
8 4550
 
3.5%
15 4473
 
3.4%
11 4420
 
3.4%
Other values (21) 83122
63.6%
2025-02-10T13:46:59.639437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 61429
27.3%
2 53857
24.0%
3 19502
 
8.7%
7 13732
 
6.1%
8 13721
 
6.1%
6 13069
 
5.8%
0 12986
 
5.8%
4 12423
 
5.5%
9 12062
 
5.4%
5 11937
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 61429
27.3%
2 53857
24.0%
3 19502
 
8.7%
7 13732
 
6.1%
8 13721
 
6.1%
6 13069
 
5.8%
0 12986
 
5.8%
4 12423
 
5.5%
9 12062
 
5.4%
5 11937
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 61429
27.3%
2 53857
24.0%
3 19502
 
8.7%
7 13732
 
6.1%
8 13721
 
6.1%
6 13069
 
5.8%
0 12986
 
5.8%
4 12423
 
5.5%
9 12062
 
5.4%
5 11937
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 61429
27.3%
2 53857
24.0%
3 19502
 
8.7%
7 13732
 
6.1%
8 13721
 
6.1%
6 13069
 
5.8%
0 12986
 
5.8%
4 12423
 
5.5%
9 12062
 
5.4%
5 11937
 
5.3%

verbatimEventDate
Text

Missing 

Distinct17805
Distinct (%)6.4%
Missing445814
Missing (%)61.5%
Memory size5.5 MiB
2025-02-10T13:46:59.785328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length61
Median length11
Mean length11.41229808
Min length4

Characters and Unicode

Total characters3180539
Distinct characters69
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

Unique5871 ?
Unique (%)2.1%

Sample

1st row23 JAN 1985
2nd rowApril, 1928
3rd row-- --- 1980
4th row-- --- 1963
5th row-- --- 1956
ValueCountFrequency (%)
235730
28.9%
aug 23677
 
2.9%
jul 22916
 
2.8%
summer 20031
 
2.5%
jun 14619
 
1.8%
may 14325
 
1.8%
oct 14287
 
1.7%
to 13955
 
1.7%
sep 13176
 
1.6%
apr 10764
 
1.3%
Other values (1210) 433163
53.0%
2025-02-10T13:46:59.997975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 633590
19.9%
537949
16.9%
1 382844
12.0%
9 314473
9.9%
8 105770
 
3.3%
0 101858
 
3.2%
7 96225
 
3.0%
2 94879
 
3.0%
6 69663
 
2.2%
A 63864
 
2.0%
Other values (59) 779424
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3180539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 633590
19.9%
537949
16.9%
1 382844
12.0%
9 314473
9.9%
8 105770
 
3.3%
0 101858
 
3.2%
7 96225
 
3.0%
2 94879
 
3.0%
6 69663
 
2.2%
A 63864
 
2.0%
Other values (59) 779424
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3180539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 633590
19.9%
537949
16.9%
1 382844
12.0%
9 314473
9.9%
8 105770
 
3.3%
0 101858
 
3.2%
7 96225
 
3.0%
2 94879
 
3.0%
6 69663
 
2.2%
A 63864
 
2.0%
Other values (59) 779424
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3180539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 633590
19.9%
537949
16.9%
1 382844
12.0%
9 314473
9.9%
8 105770
 
3.3%
0 101858
 
3.2%
7 96225
 
3.0%
2 94879
 
3.0%
6 69663
 
2.2%
A 63864
 
2.0%
Other values (59) 779424
24.5%

locationID
Text

Missing 

Distinct66560
Distinct (%)17.1%
Missing335037
Missing (%)46.2%
Memory size5.5 MiB
2025-02-10T13:47:00.159250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length61
Median length59
Mean length5.757204002
Min length1

Characters and Unicode

Total characters2242264
Distinct characters81
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

Unique40451 ?
Unique (%)10.4%

Sample

1st row1612
2nd row06
3rd rowUSGS LOC M533
4th row42246
5th row707A
ValueCountFrequency (%)
42246 30863
 
6.4%
35k 30551
 
6.3%
loc 19929
 
4.1%
sta 7656
 
1.6%
d 5640
 
1.2%
site 4020
 
0.8%
40193 3269
 
0.7%
leg 3132
 
0.7%
olson 2904
 
0.6%
41142 2897
 
0.6%
Other values (59519) 370823
77.0%
2025-02-10T13:47:00.394335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 252324
 
11.3%
1 209625
 
9.3%
4 194523
 
8.7%
3 152357
 
6.8%
0 140257
 
6.3%
5 136706
 
6.1%
6 130433
 
5.8%
7 107242
 
4.8%
8 99787
 
4.5%
9 93127
 
4.2%
Other values (71) 725883
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2242264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 252324
 
11.3%
1 209625
 
9.3%
4 194523
 
8.7%
3 152357
 
6.8%
0 140257
 
6.3%
5 136706
 
6.1%
6 130433
 
5.8%
7 107242
 
4.8%
8 99787
 
4.5%
9 93127
 
4.2%
Other values (71) 725883
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2242264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 252324
 
11.3%
1 209625
 
9.3%
4 194523
 
8.7%
3 152357
 
6.8%
0 140257
 
6.3%
5 136706
 
6.1%
6 130433
 
5.8%
7 107242
 
4.8%
8 99787
 
4.5%
9 93127
 
4.2%
Other values (71) 725883
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2242264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 252324
 
11.3%
1 209625
 
9.3%
4 194523
 
8.7%
3 152357
 
6.8%
0 140257
 
6.3%
5 136706
 
6.1%
6 130433
 
5.8%
7 107242
 
4.8%
8 99787
 
4.5%
9 93127
 
4.2%
Other values (71) 725883
32.4%

higherGeography
Text

Missing 

Distinct4708
Distinct (%)0.8%
Missing148417
Missing (%)20.5%
Memory size5.5 MiB
2025-02-10T13:47:00.544908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length111
Median length97
Mean length42.17362361
Min length4

Characters and Unicode

Total characters24295845
Distinct characters68
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

Unique1213 ?
Unique (%)0.2%

Sample

1st rowNorth America, United States, Florida
2nd rowAfrica, Kenya, Marsabit
3rd rowNorth America, United States, Nevada, Pershing County
4th rowCuba, Camaguey Prov
5th rowNorth America, United States, North Carolina, Beaufort County
ValueCountFrequency (%)
north 537307
16.4%
america 480121
14.7%
united 421781
12.9%
states 421705
12.9%
county 259124
 
7.9%
carolina 46843
 
1.4%
canada 38942
 
1.2%
texas 38273
 
1.2%
colorado 35917
 
1.1%
beaufort 33680
 
1.0%
Other values (2951) 959718
29.3%
2025-02-10T13:47:00.769046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2697320
 
11.1%
t 2343978
 
9.6%
a 2051368
 
8.4%
e 1823223
 
7.5%
i 1571709
 
6.5%
r 1497295
 
6.2%
o 1387848
 
5.7%
, 1279367
 
5.3%
n 1260166
 
5.2%
s 766919
 
3.2%
Other values (58) 7616652
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24295845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2697320
 
11.1%
t 2343978
 
9.6%
a 2051368
 
8.4%
e 1823223
 
7.5%
i 1571709
 
6.5%
r 1497295
 
6.2%
o 1387848
 
5.7%
, 1279367
 
5.3%
n 1260166
 
5.2%
s 766919
 
3.2%
Other values (58) 7616652
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24295845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2697320
 
11.1%
t 2343978
 
9.6%
a 2051368
 
8.4%
e 1823223
 
7.5%
i 1571709
 
6.5%
r 1497295
 
6.2%
o 1387848
 
5.7%
, 1279367
 
5.3%
n 1260166
 
5.2%
s 766919
 
3.2%
Other values (58) 7616652
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24295845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2697320
 
11.1%
t 2343978
 
9.6%
a 2051368
 
8.4%
e 1823223
 
7.5%
i 1571709
 
6.5%
r 1497295
 
6.2%
o 1387848
 
5.7%
, 1279367
 
5.3%
n 1260166
 
5.2%
s 766919
 
3.2%
Other values (58) 7616652
31.3%

continent
Text

Missing 

Distinct44
Distinct (%)< 0.1%
Missing210428
Missing (%)29.0%
Memory size5.5 MiB
2025-02-10T13:47:00.804767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length13
Mean length13.19896709
Min length4

Characters and Unicode

Total characters6785325
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowNorth America
2nd rowAfrica
3rd rowNorth America
4th rowNorth America
5th rowNorth America
ValueCountFrequency (%)
north 491990
47.1%
america 480118
46.0%
ocean 26667
 
2.6%
atlantic 13621
 
1.3%
south 9893
 
0.9%
pacific 8356
 
0.8%
indian 4034
 
0.4%
africa 3468
 
0.3%
oceania 2870
 
0.3%
europe 1626
 
0.2%
Other values (7) 1509
 
0.1%
2025-02-10T13:47:00.901155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 977899
14.4%
c 544584
8.0%
a 542896
8.0%
530072
7.8%
t 529855
7.8%
i 522205
7.7%
e 511408
7.5%
o 503636
7.4%
h 502009
7.4%
A 498588
7.3%
Other values (16) 1122173
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6785325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 977899
14.4%
c 544584
8.0%
a 542896
8.0%
530072
7.8%
t 529855
7.8%
i 522205
7.7%
e 511408
7.5%
o 503636
7.4%
h 502009
7.4%
A 498588
7.3%
Other values (16) 1122173
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6785325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 977899
14.4%
c 544584
8.0%
a 542896
8.0%
530072
7.8%
t 529855
7.8%
i 522205
7.7%
e 511408
7.5%
o 503636
7.4%
h 502009
7.4%
A 498588
7.3%
Other values (16) 1122173
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6785325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 977899
14.4%
c 544584
8.0%
a 542896
8.0%
530072
7.8%
t 529855
7.8%
i 522205
7.7%
e 511408
7.5%
o 503636
7.4%
h 502009
7.4%
A 498588
7.3%
Other values (16) 1122173
16.5%

waterBody
Text

Missing 

Distinct172
Distinct (%)0.6%
Missing696851
Missing (%)96.2%
Memory size5.5 MiB
2025-02-10T13:47:00.934419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length61
Median length54
Mean length21.95758759
Min length8

Characters and Unicode

Total characters607281
Distinct characters49
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

Unique58 ?
Unique (%)0.2%

Sample

1st rowNorth Atlantic Ocean
2nd rowNorth Pacific Ocean
3rd rowNorth Atlantic Ocean, Caribbean Sea
4th rowNorth Atlantic Ocean
5th rowNorth Atlantic Ocean
ValueCountFrequency (%)
ocean 26667
28.1%
north 18835
19.9%
atlantic 13621
14.4%
pacific 8356
 
8.8%
sea 5778
 
6.1%
indian 4034
 
4.3%
south 2993
 
3.2%
timor 2479
 
2.6%
of 2181
 
2.3%
gulf 2067
 
2.2%
Other values (146) 7758
 
8.2%
2025-02-10T13:47:01.031873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67112
11.1%
a 66029
10.9%
c 60399
9.9%
n 52729
 
8.7%
t 51240
 
8.4%
i 42959
 
7.1%
e 39252
 
6.5%
o 28732
 
4.7%
O 27050
 
4.5%
r 26329
 
4.3%
Other values (39) 145450
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 607281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
67112
11.1%
a 66029
10.9%
c 60399
9.9%
n 52729
 
8.7%
t 51240
 
8.4%
i 42959
 
7.1%
e 39252
 
6.5%
o 28732
 
4.7%
O 27050
 
4.5%
r 26329
 
4.3%
Other values (39) 145450
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 607281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
67112
11.1%
a 66029
10.9%
c 60399
9.9%
n 52729
 
8.7%
t 51240
 
8.4%
i 42959
 
7.1%
e 39252
 
6.5%
o 28732
 
4.7%
O 27050
 
4.5%
r 26329
 
4.3%
Other values (39) 145450
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 607281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
67112
11.1%
a 66029
10.9%
c 60399
9.9%
n 52729
 
8.7%
t 51240
 
8.4%
i 42959
 
7.1%
e 39252
 
6.5%
o 28732
 
4.7%
O 27050
 
4.5%
r 26329
 
4.3%
Other values (39) 145450
24.0%

islandGroup
Text

Missing 

Distinct33
Distinct (%)4.1%
Missing723710
Missing (%)99.9%
Memory size5.5 MiB
2025-02-10T13:47:01.069228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length24
Mean length16.78571429
Min length5

Characters and Unicode

Total characters13395
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.6%

Sample

1st rowMariana Islands
2nd rowNorthern Mariana Islands
3rd rowGilbert Islands
4th rowGilbert Islands
5th rowAleutian Islands
ValueCountFrequency (%)
islands 765
44.5%
marshall 241
 
14.0%
mariana 155
 
9.0%
gilbert 135
 
7.9%
northern 134
 
7.8%
marianas 120
 
7.0%
solomon 21
 
1.2%
ryukyu 18
 
1.0%
hawaiian 18
 
1.0%
antilles 15
 
0.9%
Other values (26) 97
 
5.6%
2025-02-10T13:47:01.177819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2202
16.4%
s 1936
14.5%
l 1461
10.9%
n 1270
9.5%
r 960
7.2%
921
6.9%
d 800
 
6.0%
I 765
 
5.7%
M 527
 
3.9%
i 498
 
3.7%
Other values (36) 2055
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2202
16.4%
s 1936
14.5%
l 1461
10.9%
n 1270
9.5%
r 960
7.2%
921
6.9%
d 800
 
6.0%
I 765
 
5.7%
M 527
 
3.9%
i 498
 
3.7%
Other values (36) 2055
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2202
16.4%
s 1936
14.5%
l 1461
10.9%
n 1270
9.5%
r 960
7.2%
921
6.9%
d 800
 
6.0%
I 765
 
5.7%
M 527
 
3.9%
i 498
 
3.7%
Other values (36) 2055
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2202
16.4%
s 1936
14.5%
l 1461
10.9%
n 1270
9.5%
r 960
7.2%
921
6.9%
d 800
 
6.0%
I 765
 
5.7%
M 527
 
3.9%
i 498
 
3.7%
Other values (36) 2055
15.3%

island
Text

Missing 

Distinct87
Distinct (%)0.9%
Missing714401
Missing (%)98.6%
Memory size5.5 MiB
2025-02-10T13:47:01.209073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length4
Mean length6.015335906
Min length3

Characters and Unicode

Total characters60797
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

Unique38 ?
Unique (%)0.4%

Sample

1st rowOahu
2nd rowOahu
3rd rowOahu
4th rowAnimasola Island
5th rowMolokai
ValueCountFrequency (%)
oahu 5926
51.1%
molokai 2218
 
19.1%
saint 944
 
8.1%
helena 938
 
8.1%
atoll 241
 
2.1%
saipan 132
 
1.1%
guam 129
 
1.1%
onotoa 116
 
1.0%
martha's 108
 
0.9%
vineyard 108
 
0.9%
Other values (91) 728
 
6.3%
2025-02-10T13:47:01.306459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11360
18.7%
u 6232
10.3%
h 6099
10.0%
O 6043
9.9%
o 5165
8.5%
i 4062
 
6.7%
l 3813
 
6.3%
n 2689
 
4.4%
k 2476
 
4.1%
M 2342
 
3.9%
Other values (40) 10516
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11360
18.7%
u 6232
10.3%
h 6099
10.0%
O 6043
9.9%
o 5165
8.5%
i 4062
 
6.7%
l 3813
 
6.3%
n 2689
 
4.4%
k 2476
 
4.1%
M 2342
 
3.9%
Other values (40) 10516
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11360
18.7%
u 6232
10.3%
h 6099
10.0%
O 6043
9.9%
o 5165
8.5%
i 4062
 
6.7%
l 3813
 
6.3%
n 2689
 
4.4%
k 2476
 
4.1%
M 2342
 
3.9%
Other values (40) 10516
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11360
18.7%
u 6232
10.3%
h 6099
10.0%
O 6043
9.9%
o 5165
8.5%
i 4062
 
6.7%
l 3813
 
6.3%
n 2689
 
4.4%
k 2476
 
4.1%
M 2342
 
3.9%
Other values (40) 10516
17.3%

country
Text

Missing 

Distinct227
Distinct (%)< 0.1%
Missing173269
Missing (%)23.9%
Memory size5.5 MiB
2025-02-10T13:47:01.397701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length44
Median length13
Mean length11.8822108
Min length4

Characters and Unicode

Total characters6549938
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

Unique39 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowKenya
3rd rowUnited States
4th rowCuba
5th rowUnited States
ValueCountFrequency (%)
united 421781
42.0%
states 421705
42.0%
canada 38942
 
3.9%
panama 8607
 
0.9%
republic 6480
 
0.6%
dominican 6290
 
0.6%
islands 4307
 
0.4%
mexico 3812
 
0.4%
colombia 3579
 
0.4%
france 3529
 
0.4%
Other values (228) 84524
 
8.4%
2025-02-10T13:47:01.499978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1291649
19.7%
e 891107
13.6%
a 672519
10.3%
n 536738
8.2%
i 496752
 
7.6%
d 485872
 
7.4%
s 453446
 
6.9%
452317
 
6.9%
S 427898
 
6.5%
U 422899
 
6.5%
Other values (47) 418741
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6549938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1291649
19.7%
e 891107
13.6%
a 672519
10.3%
n 536738
8.2%
i 496752
 
7.6%
d 485872
 
7.4%
s 453446
 
6.9%
452317
 
6.9%
S 427898
 
6.5%
U 422899
 
6.5%
Other values (47) 418741
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6549938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1291649
19.7%
e 891107
13.6%
a 672519
10.3%
n 536738
8.2%
i 496752
 
7.6%
d 485872
 
7.4%
s 453446
 
6.9%
452317
 
6.9%
S 427898
 
6.5%
U 422899
 
6.5%
Other values (47) 418741
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6549938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1291649
19.7%
e 891107
13.6%
a 672519
10.3%
n 536738
8.2%
i 496752
 
7.6%
d 485872
 
7.4%
s 453446
 
6.9%
452317
 
6.9%
S 427898
 
6.5%
U 422899
 
6.5%
Other values (47) 418741
 
6.4%

stateProvince
Text

Missing 

Distinct892
Distinct (%)0.2%
Missing226462
Missing (%)31.3%
Memory size5.5 MiB
2025-02-10T13:47:01.648163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length23
Mean length8.789222281
Min length3

Characters and Unicode

Total characters4377437
Distinct characters64
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

Unique236 ?
Unique (%)< 0.1%

Sample

1st rowFlorida
2nd rowMarsabit
3rd rowNevada
4th rowCamaguey Prov
5th rowNorth Carolina
ValueCountFrequency (%)
carolina 46813
 
7.5%
north 45129
 
7.2%
texas 38253
 
6.1%
colorado 35917
 
5.8%
california 32474
 
5.2%
columbia 32203
 
5.2%
british 32085
 
5.1%
alaska 28545
 
4.6%
new 23155
 
3.7%
wyoming 22778
 
3.6%
Other values (878) 287106
46.0%
2025-02-10T13:47:01.864071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 622536
14.2%
i 445132
 
10.2%
o 412678
 
9.4%
r 299951
 
6.9%
n 262321
 
6.0%
l 249350
 
5.7%
s 213346
 
4.9%
e 190372
 
4.3%
C 155417
 
3.6%
t 143584
 
3.3%
Other values (54) 1382750
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 622536
14.2%
i 445132
 
10.2%
o 412678
 
9.4%
r 299951
 
6.9%
n 262321
 
6.0%
l 249350
 
5.7%
s 213346
 
4.9%
e 190372
 
4.3%
C 155417
 
3.6%
t 143584
 
3.3%
Other values (54) 1382750
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 622536
14.2%
i 445132
 
10.2%
o 412678
 
9.4%
r 299951
 
6.9%
n 262321
 
6.0%
l 249350
 
5.7%
s 213346
 
4.9%
e 190372
 
4.3%
C 155417
 
3.6%
t 143584
 
3.3%
Other values (54) 1382750
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 622536
14.2%
i 445132
 
10.2%
o 412678
 
9.4%
r 299951
 
6.9%
n 262321
 
6.0%
l 249350
 
5.7%
s 213346
 
4.9%
e 190372
 
4.3%
C 155417
 
3.6%
t 143584
 
3.3%
Other values (54) 1382750
31.6%

county
Text

Missing 

Distinct1997
Distinct (%)0.7%
Missing454433
Missing (%)62.7%
Memory size5.5 MiB
2025-02-10T13:47:01.902781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length29
Mean length14.2528779
Min length3

Characters and Unicode

Total characters3849346
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

Unique393 ?
Unique (%)0.1%

Sample

1st rowPershing County
2nd rowBeaufort County
3rd rowBrewster County
4th rowLos Angeles County
5th rowHonolulu County
ValueCountFrequency (%)
county 259124
45.6%
beaufort 33592
 
5.9%
brewster 15677
 
2.8%
maui 10401
 
1.8%
los 8883
 
1.6%
angeles 8865
 
1.6%
honolulu 5926
 
1.0%
san 4953
 
0.9%
lincoln 4346
 
0.8%
culberson 4132
 
0.7%
Other values (1945) 212334
37.4%
2025-02-10T13:47:01.997924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 423340
11.0%
n 401510
10.4%
t 375302
9.7%
u 352655
9.2%
298158
 
7.7%
C 289740
 
7.5%
y 279783
 
7.3%
e 215178
 
5.6%
a 186491
 
4.8%
r 177010
 
4.6%
Other values (55) 850179
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3849346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 423340
11.0%
n 401510
10.4%
t 375302
9.7%
u 352655
9.2%
298158
 
7.7%
C 289740
 
7.5%
y 279783
 
7.3%
e 215178
 
5.6%
a 186491
 
4.8%
r 177010
 
4.6%
Other values (55) 850179
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3849346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 423340
11.0%
n 401510
10.4%
t 375302
9.7%
u 352655
9.2%
298158
 
7.7%
C 289740
 
7.5%
y 279783
 
7.3%
e 215178
 
5.6%
a 186491
 
4.8%
r 177010
 
4.6%
Other values (55) 850179
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3849346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 423340
11.0%
n 401510
10.4%
t 375302
9.7%
u 352655
9.2%
298158
 
7.7%
C 289740
 
7.5%
y 279783
 
7.3%
e 215178
 
5.6%
a 186491
 
4.8%
r 177010
 
4.6%
Other values (55) 850179
22.1%

locality
Text

Missing 

Distinct31755
Distinct (%)19.4%
Missing560871
Missing (%)77.4%
Memory size5.5 MiB
2025-02-10T13:47:02.158469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length471
Median length316
Mean length59.79365302
Min length1

Characters and Unicode

Total characters9784454
Distinct characters100
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

Unique21088 ?
Unique (%)12.9%

Sample

1st rowSt. Andrew Bay
2nd rowNuevitas Bay, Between Nuevitas And Pastelillo
3rd rowPalos Verdes Hills; East side of Deadman's Island
4th rowNorth slope of San Pedro Hills, ravine S of harbor City, 4200 feet N and 53.5 degrees E from 342-foot hill, 100 feet up ravine from end of Bellepoint Street (W98-30)
5th rowCoyote Springs Valley; spring
ValueCountFrequency (%)
of 120156
 
7.0%
34919
 
2.0%
and 22265
 
1.3%
bay 19665
 
1.1%
the 18421
 
1.1%
on 17778
 
1.0%
from 16823
 
1.0%
n 16777
 
1.0%
feet 15757
 
0.9%
river 15334
 
0.9%
Other values (34131) 1421831
82.7%
2025-02-10T13:47:02.388204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1556089
 
15.9%
e 696361
 
7.1%
a 667574
 
6.8%
o 563183
 
5.8%
n 459218
 
4.7%
t 454511
 
4.6%
r 411334
 
4.2%
i 400897
 
4.1%
l 325764
 
3.3%
s 321111
 
3.3%
Other values (90) 3928412
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9784454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1556089
 
15.9%
e 696361
 
7.1%
a 667574
 
6.8%
o 563183
 
5.8%
n 459218
 
4.7%
t 454511
 
4.6%
r 411334
 
4.2%
i 400897
 
4.1%
l 325764
 
3.3%
s 321111
 
3.3%
Other values (90) 3928412
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9784454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1556089
 
15.9%
e 696361
 
7.1%
a 667574
 
6.8%
o 563183
 
5.8%
n 459218
 
4.7%
t 454511
 
4.6%
r 411334
 
4.2%
i 400897
 
4.1%
l 325764
 
3.3%
s 321111
 
3.3%
Other values (90) 3928412
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9784454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1556089
 
15.9%
e 696361
 
7.1%
a 667574
 
6.8%
o 563183
 
5.8%
n 459218
 
4.7%
t 454511
 
4.6%
r 411334
 
4.2%
i 400897
 
4.1%
l 325764
 
3.3%
s 321111
 
3.3%
Other values (90) 3928412
40.1%

verbatimElevation
Text

Missing 

Distinct7
Distinct (%)3.6%
Missing724311
Missing (%)> 99.9%
Memory size5.5 MiB
2025-02-10T13:47:02.440797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length88
Median length88
Mean length81.14720812
Min length8

Characters and Unicode

Total characters15986
Distinct characters55
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 (%)1.0%

Sample

1st rowElevation for Rampart Cave derived from Google Earth by Dr. Jim Mead on 4 Decemeber 2023
2nd rowApprox.450-500ft Above Base Of Fm
3rd rowElevation for Rampart Cave derived from Google Earth by Dr. Jim Mead on 4 Decemeber 2023
4th rowElevation for Rampart Cave derived from Google Earth by Dr. Jim Mead on 4 Decemeber 2023
5th rowElevation for Rampart Cave derived from Google Earth by Dr. Jim Mead on 4 Decemeber 2023
ValueCountFrequency (%)
elevation 161
 
5.5%
by 161
 
5.5%
2023 161
 
5.5%
decemeber 161
 
5.5%
4 161
 
5.5%
mead 161
 
5.5%
jim 161
 
5.5%
dr 161
 
5.5%
on 161
 
5.5%
earth 161
 
5.5%
Other values (38) 1300
44.7%
2025-02-10T13:47:02.557701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2713
17.0%
e 1696
 
10.6%
r 1185
 
7.4%
o 1092
 
6.8%
a 1023
 
6.4%
m 656
 
4.1%
t 562
 
3.5%
v 533
 
3.3%
i 527
 
3.3%
d 497
 
3.1%
Other values (45) 5502
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2713
17.0%
e 1696
 
10.6%
r 1185
 
7.4%
o 1092
 
6.8%
a 1023
 
6.4%
m 656
 
4.1%
t 562
 
3.5%
v 533
 
3.3%
i 527
 
3.3%
d 497
 
3.1%
Other values (45) 5502
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2713
17.0%
e 1696
 
10.6%
r 1185
 
7.4%
o 1092
 
6.8%
a 1023
 
6.4%
m 656
 
4.1%
t 562
 
3.5%
v 533
 
3.3%
i 527
 
3.3%
d 497
 
3.1%
Other values (45) 5502
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2713
17.0%
e 1696
 
10.6%
r 1185
 
7.4%
o 1092
 
6.8%
a 1023
 
6.4%
m 656
 
4.1%
t 562
 
3.5%
v 533
 
3.3%
i 527
 
3.3%
d 497
 
3.1%
Other values (45) 5502
34.4%

verbatimDepth
Text

Missing 

Distinct17
Distinct (%)20.2%
Missing724424
Missing (%)> 99.9%
Memory size5.5 MiB
2025-02-10T13:47:02.592538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length10
Mean length5.523809524
Min length4

Characters and Unicode

Total characters464
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)10.7%

Sample

1st rowreef
2nd rowBeach
3rd row?48 Ms
4th rowBeach
5th rowIntertidal
ValueCountFrequency (%)
reef 30
27.5%
beach 25
22.9%
low 9
 
8.3%
ms 8
 
7.3%
water 7
 
6.4%
48 6
 
5.5%
no.4 4
 
3.7%
mnb 3
 
2.8%
57ms 2
 
1.8%
25 2
 
1.8%
Other values (12) 13
11.9%
2025-02-10T13:47:02.684775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 96
20.7%
r 40
 
8.6%
a 37
 
8.0%
f 31
 
6.7%
c 26
 
5.6%
h 25
 
5.4%
25
 
5.4%
b 18
 
3.9%
o 13
 
2.8%
t 13
 
2.8%
Other values (30) 140
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 96
20.7%
r 40
 
8.6%
a 37
 
8.0%
f 31
 
6.7%
c 26
 
5.6%
h 25
 
5.4%
25
 
5.4%
b 18
 
3.9%
o 13
 
2.8%
t 13
 
2.8%
Other values (30) 140
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 96
20.7%
r 40
 
8.6%
a 37
 
8.0%
f 31
 
6.7%
c 26
 
5.6%
h 25
 
5.4%
25
 
5.4%
b 18
 
3.9%
o 13
 
2.8%
t 13
 
2.8%
Other values (30) 140
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 96
20.7%
r 40
 
8.6%
a 37
 
8.0%
f 31
 
6.7%
c 26
 
5.6%
h 25
 
5.4%
25
 
5.4%
b 18
 
3.9%
o 13
 
2.8%
t 13
 
2.8%
Other values (30) 140
30.2%

decimalLatitude
Text

Missing 

Distinct34307
Distinct (%)33.0%
Missing620569
Missing (%)85.7%
Memory size5.5 MiB
2025-02-10T13:47:02.838157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.719883778
Min length3

Characters and Unicode

Total characters698458
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

Unique19066 ?
Unique (%)18.3%

Sample

1st row30.1564
2nd row36.9858
3rd row31.9911
4th row69.08
5th row17.8883
ValueCountFrequency (%)
44.6458 1686
 
1.6%
17.5 673
 
0.6%
29.8119 329
 
0.3%
33.1767 323
 
0.3%
34.6405 307
 
0.3%
38.8295 287
 
0.3%
41.1458 279
 
0.3%
48.1104 243
 
0.2%
40.6184 235
 
0.2%
31.6767 227
 
0.2%
Other values (34049) 99350
95.6%
2025-02-10T13:47:03.047754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 103939
14.9%
3 93842
13.4%
4 66308
9.5%
5 65933
9.4%
8 57884
8.3%
1 55433
7.9%
7 55155
7.9%
6 54645
7.8%
2 54452
7.8%
9 45816
6.6%
Other values (2) 45051
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 698458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 103939
14.9%
3 93842
13.4%
4 66308
9.5%
5 65933
9.4%
8 57884
8.3%
1 55433
7.9%
7 55155
7.9%
6 54645
7.8%
2 54452
7.8%
9 45816
6.6%
Other values (2) 45051
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 698458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 103939
14.9%
3 93842
13.4%
4 66308
9.5%
5 65933
9.4%
8 57884
8.3%
1 55433
7.9%
7 55155
7.9%
6 54645
7.8%
2 54452
7.8%
9 45816
6.6%
Other values (2) 45051
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 698458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 103939
14.9%
3 93842
13.4%
4 66308
9.5%
5 65933
9.4%
8 57884
8.3%
1 55433
7.9%
7 55155
7.9%
6 54645
7.8%
2 54452
7.8%
9 45816
6.6%
Other values (2) 45051
6.5%

decimalLongitude
Text

Missing 

Distinct35344
Distinct (%)34.0%
Missing620569
Missing (%)85.7%
Memory size5.5 MiB
2025-02-10T13:47:03.207578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.641020214
Min length3

Characters and Unicode

Total characters794200
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

Unique19861 ?
Unique (%)19.1%

Sample

1st row-85.6439
2nd row-114.996
3rd row-80.7842
4th row-155.83
5th row-66.52
ValueCountFrequency (%)
123.908 1686
 
1.6%
95.0833 673
 
0.6%
103.252 329
 
0.3%
98.6878 321
 
0.3%
105.851 307
 
0.3%
76.8473 287
 
0.3%
115.358 279
 
0.3%
123.934 243
 
0.2%
108.207 235
 
0.2%
123.18 230
 
0.2%
Other values (35142) 99349
95.6%
2025-02-10T13:47:03.424846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 103939
13.1%
- 95620
12.0%
1 88364
11.1%
7 72540
9.1%
8 71709
9.0%
3 62429
7.9%
6 55880
7.0%
5 55457
7.0%
2 52919
6.7%
9 50099
6.3%
Other values (2) 85244
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 794200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 103939
13.1%
- 95620
12.0%
1 88364
11.1%
7 72540
9.1%
8 71709
9.0%
3 62429
7.9%
6 55880
7.0%
5 55457
7.0%
2 52919
6.7%
9 50099
6.3%
Other values (2) 85244
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 794200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 103939
13.1%
- 95620
12.0%
1 88364
11.1%
7 72540
9.1%
8 71709
9.0%
3 62429
7.9%
6 55880
7.0%
5 55457
7.0%
2 52919
6.7%
9 50099
6.3%
Other values (2) 85244
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 794200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 103939
13.1%
- 95620
12.0%
1 88364
11.1%
7 72540
9.1%
8 71709
9.0%
3 62429
7.9%
6 55880
7.0%
5 55457
7.0%
2 52919
6.7%
9 50099
6.3%
Other values (2) 85244
10.7%

geodeticDatum
Text

Missing 

Distinct5
Distinct (%)< 0.1%
Missing698201
Missing (%)96.4%
Memory size5.5 MiB
2025-02-10T13:47:03.465910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.69483407
Min length5

Characters and Unicode

Total characters465498
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWGS 84 (EPSG:4326)
2nd rowWGS 84 (EPSG:4326)
3rd rowWGS 84 (EPSG:4326)
4th rowWGS 84 (EPSG:4326)
5th rowWGS 84 (EPSG:4326)
ValueCountFrequency (%)
wgs 24628
32.1%
84 24628
32.1%
epsg:4326 24628
32.1%
nad27 561
 
0.7%
epsg:4267 561
 
0.7%
nad83 474
 
0.6%
epsg:4269 474
 
0.6%
wgs84 447
 
0.6%
not 197
 
0.3%
recorded 197
 
0.3%
2025-02-10T13:47:03.559069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 50738
10.9%
S 50738
10.9%
4 50738
10.9%
50488
10.8%
2 26224
 
5.6%
) 25663
 
5.5%
( 25663
 
5.5%
E 25663
 
5.5%
P 25663
 
5.5%
: 25663
 
5.5%
Other values (16) 108257
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 465498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 50738
10.9%
S 50738
10.9%
4 50738
10.9%
50488
10.8%
2 26224
 
5.6%
) 25663
 
5.5%
( 25663
 
5.5%
E 25663
 
5.5%
P 25663
 
5.5%
: 25663
 
5.5%
Other values (16) 108257
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 465498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 50738
10.9%
S 50738
10.9%
4 50738
10.9%
50488
10.8%
2 26224
 
5.6%
) 25663
 
5.5%
( 25663
 
5.5%
E 25663
 
5.5%
P 25663
 
5.5%
: 25663
 
5.5%
Other values (16) 108257
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 465498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 50738
10.9%
S 50738
10.9%
4 50738
10.9%
50488
10.8%
2 26224
 
5.6%
) 25663
 
5.5%
( 25663
 
5.5%
E 25663
 
5.5%
P 25663
 
5.5%
: 25663
 
5.5%
Other values (16) 108257
23.3%

verbatimLatitude
Text

Missing 

Distinct2
Distinct (%)40.0%
Missing724503
Missing (%)> 99.9%
Memory size5.5 MiB
2025-02-10T13:47:03.590336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.4
Min length9

Characters and Unicode

Total characters47
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

Unique0 ?
Unique (%)0.0%

Sample

1st row11 53.4 N
2nd row11 53.4 N
3rd row11 53.4 N
4th row18 44.98 N
5th row18 44.98 N
ValueCountFrequency (%)
n 5
33.3%
11 3
20.0%
53.4 3
20.0%
18 2
 
13.3%
44.98 2
 
13.3%
2025-02-10T13:47:03.675584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
21.3%
1 8
17.0%
4 7
14.9%
. 5
10.6%
N 5
10.6%
8 4
 
8.5%
5 3
 
6.4%
3 3
 
6.4%
9 2
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10
21.3%
1 8
17.0%
4 7
14.9%
. 5
10.6%
N 5
10.6%
8 4
 
8.5%
5 3
 
6.4%
3 3
 
6.4%
9 2
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10
21.3%
1 8
17.0%
4 7
14.9%
. 5
10.6%
N 5
10.6%
8 4
 
8.5%
5 3
 
6.4%
3 3
 
6.4%
9 2
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10
21.3%
1 8
17.0%
4 7
14.9%
. 5
10.6%
N 5
10.6%
8 4
 
8.5%
5 3
 
6.4%
3 3
 
6.4%
9 2
 
4.3%

verbatimLongitude
Text

Missing 

Distinct2
Distinct (%)40.0%
Missing724503
Missing (%)> 99.9%
Memory size5.5 MiB
2025-02-10T13:47:03.708014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.4
Min length9

Characters and Unicode

Total characters47
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

Unique0 ?
Unique (%)0.0%

Sample

1st row48 14.7 E
2nd row48 14.7 E
3rd row48 14.7 E
4th row60 07.78 E
5th row60 07.78 E
ValueCountFrequency (%)
e 5
33.3%
48 3
20.0%
14.7 3
20.0%
60 2
 
13.3%
07.78 2
 
13.3%
2025-02-10T13:47:03.794928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
21.3%
7 7
14.9%
4 6
12.8%
8 5
10.6%
. 5
10.6%
E 5
10.6%
0 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10
21.3%
7 7
14.9%
4 6
12.8%
8 5
10.6%
. 5
10.6%
E 5
10.6%
0 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10
21.3%
7 7
14.9%
4 6
12.8%
8 5
10.6%
. 5
10.6%
E 5
10.6%
0 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10
21.3%
7 7
14.9%
4 6
12.8%
8 5
10.6%
. 5
10.6%
E 5
10.6%
0 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%

verbatimCoordinateSystem
Text

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing654265
Missing (%)90.3%
Memory size5.5 MiB
2025-02-10T13:47:03.824888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters1615589
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 70243
33.3%
minutes 70243
33.3%
seconds 70243
33.3%
2025-02-10T13:47:03.910828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 351215
21.7%
s 210729
13.0%
140486
 
8.7%
n 140486
 
8.7%
D 70243
 
4.3%
g 70243
 
4.3%
r 70243
 
4.3%
M 70243
 
4.3%
i 70243
 
4.3%
u 70243
 
4.3%
Other values (5) 351215
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1615589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 351215
21.7%
s 210729
13.0%
140486
 
8.7%
n 140486
 
8.7%
D 70243
 
4.3%
g 70243
 
4.3%
r 70243
 
4.3%
M 70243
 
4.3%
i 70243
 
4.3%
u 70243
 
4.3%
Other values (5) 351215
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1615589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 351215
21.7%
s 210729
13.0%
140486
 
8.7%
n 140486
 
8.7%
D 70243
 
4.3%
g 70243
 
4.3%
r 70243
 
4.3%
M 70243
 
4.3%
i 70243
 
4.3%
u 70243
 
4.3%
Other values (5) 351215
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1615589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 351215
21.7%
s 210729
13.0%
140486
 
8.7%
n 140486
 
8.7%
D 70243
 
4.3%
g 70243
 
4.3%
r 70243
 
4.3%
M 70243
 
4.3%
i 70243
 
4.3%
u 70243
 
4.3%
Other values (5) 351215
21.7%

georeferenceProtocol
Text

Missing 

Distinct19
Distinct (%)0.1%
Missing695012
Missing (%)95.9%
Memory size5.5 MiB
2025-02-10T13:47:03.950480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length81
Median length43
Mean length42.23633713
Min length7

Characters and Unicode

Total characters1245803
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGeoreferencing Quick Reference Guide (2020)
2nd rowGeoreferencing Quick Reference Guide (2020)
3rd rowGeoreferencing Quick Reference Guide (2020)
4th rowGeoreferencing Quick Reference Guide (2020)
5th rowGeoreferencing Quick Reference Guide (2020)
ValueCountFrequency (%)
georeferencing 26344
17.6%
guide 26344
17.6%
reference 24178
16.2%
2020 24178
16.2%
quick 24178
16.2%
biogeomancer 2166
 
1.4%
2006 2166
 
1.4%
august 2166
 
1.4%
consortium 2166
 
1.4%
for 2166
 
1.4%
Other values (32) 13421
9.0%
2025-02-10T13:47:04.052548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 237471
19.1%
119977
 
9.6%
r 87730
 
7.0%
i 84069
 
6.7%
n 82720
 
6.6%
c 81302
 
6.5%
u 58822
 
4.7%
G 54854
 
4.4%
0 52731
 
4.2%
f 52688
 
4.2%
Other values (40) 333439
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1245803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 237471
19.1%
119977
 
9.6%
r 87730
 
7.0%
i 84069
 
6.7%
n 82720
 
6.6%
c 81302
 
6.5%
u 58822
 
4.7%
G 54854
 
4.4%
0 52731
 
4.2%
f 52688
 
4.2%
Other values (40) 333439
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1245803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 237471
19.1%
119977
 
9.6%
r 87730
 
7.0%
i 84069
 
6.7%
n 82720
 
6.6%
c 81302
 
6.5%
u 58822
 
4.7%
G 54854
 
4.4%
0 52731
 
4.2%
f 52688
 
4.2%
Other values (40) 333439
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1245803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 237471
19.1%
119977
 
9.6%
r 87730
 
7.0%
i 84069
 
6.7%
n 82720
 
6.6%
c 81302
 
6.5%
u 58822
 
4.7%
G 54854
 
4.4%
0 52731
 
4.2%
f 52688
 
4.2%
Other values (40) 333439
26.8%

georeferenceRemarks
Text

Missing 

Distinct2
Distinct (%)40.0%
Missing724503
Missing (%)> 99.9%
Memory size5.5 MiB
2025-02-10T13:47:04.092546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length70
Median length70
Mean length58
Min length10

Characters and Unicode

Total characters290
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

Unique1 ?
Unique (%)20.0%

Sample

1st rowA; B; C; D
2nd rowincluded in Jennifer Jett's Foram Bulk DB but not included in F Ledger
3rd rowincluded in Jennifer Jett's Foram Bulk DB but not included in F Ledger
4th rowincluded in Jennifer Jett's Foram Bulk DB but not included in F Ledger
5th rowincluded in Jennifer Jett's Foram Bulk DB but not included in F Ledger
ValueCountFrequency (%)
included 8
14.3%
in 8
14.3%
jennifer 4
7.1%
jett's 4
7.1%
foram 4
7.1%
bulk 4
7.1%
db 4
7.1%
but 4
7.1%
not 4
7.1%
f 4
7.1%
Other values (5) 8
14.3%
2025-02-10T13:47:04.184281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
17.6%
n 28
 
9.7%
e 28
 
9.7%
i 20
 
6.9%
d 20
 
6.9%
u 16
 
5.5%
t 16
 
5.5%
r 12
 
4.1%
l 12
 
4.1%
B 9
 
3.1%
Other values (17) 78
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51
17.6%
n 28
 
9.7%
e 28
 
9.7%
i 20
 
6.9%
d 20
 
6.9%
u 16
 
5.5%
t 16
 
5.5%
r 12
 
4.1%
l 12
 
4.1%
B 9
 
3.1%
Other values (17) 78
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51
17.6%
n 28
 
9.7%
e 28
 
9.7%
i 20
 
6.9%
d 20
 
6.9%
u 16
 
5.5%
t 16
 
5.5%
r 12
 
4.1%
l 12
 
4.1%
B 9
 
3.1%
Other values (17) 78
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51
17.6%
n 28
 
9.7%
e 28
 
9.7%
i 20
 
6.9%
d 20
 
6.9%
u 16
 
5.5%
t 16
 
5.5%
r 12
 
4.1%
l 12
 
4.1%
B 9
 
3.1%
Other values (17) 78
26.9%
Distinct10
Distinct (%)< 0.1%
Missing220036
Missing (%)30.4%
Memory size5.5 MiB
2025-02-10T13:47:04.286666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length8
Mean length8.387123567
Min length8

Characters and Unicode

Total characters4231069
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMesozoic
2nd rowCenozoic
3rd rowCenozoic
4th rowPaleozoic
5th rowCenozoic
ValueCountFrequency (%)
cenozoic 261752
51.9%
paleozoic 194023
38.5%
mesozoic 48343
 
9.6%
precambrian 298
 
0.1%
mesoproterozoic 41
 
< 0.1%
neoproterozoic 7
 
< 0.1%
paleoproterozoic 4
 
< 0.1%
paleoarchean 3
 
< 0.1%
mesoarchean 1
 
< 0.1%
2025-02-10T13:47:04.379684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1008448
23.8%
e 504528
11.9%
c 504472
11.9%
i 504468
11.9%
z 504170
11.9%
n 262054
 
6.2%
C 261752
 
6.2%
a 194634
 
4.6%
P 194327
 
4.6%
l 194030
 
4.6%
Other values (9) 98186
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4231069
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1008448
23.8%
e 504528
11.9%
c 504472
11.9%
i 504468
11.9%
z 504170
11.9%
n 262054
 
6.2%
C 261752
 
6.2%
a 194634
 
4.6%
P 194327
 
4.6%
l 194030
 
4.6%
Other values (9) 98186
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4231069
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1008448
23.8%
e 504528
11.9%
c 504472
11.9%
i 504468
11.9%
z 504170
11.9%
n 262054
 
6.2%
C 261752
 
6.2%
a 194634
 
4.6%
P 194327
 
4.6%
l 194030
 
4.6%
Other values (9) 98186
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4231069
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1008448
23.8%
e 504528
11.9%
c 504472
11.9%
i 504468
11.9%
z 504170
11.9%
n 262054
 
6.2%
C 261752
 
6.2%
a 194634
 
4.6%
P 194327
 
4.6%
l 194030
 
4.6%
Other values (9) 98186
 
2.3%
Distinct5
Distinct (%)0.1%
Missing718163
Missing (%)99.1%
Memory size5.5 MiB
2025-02-10T13:47:04.409378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.134121355
Min length8

Characters and Unicode

Total characters51611
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPaleozoic
2nd rowCenozoic
3rd rowMesozoic
4th rowCenozoic
5th rowCenozoic
ValueCountFrequency (%)
cenozoic 5229
82.4%
paleozoic 826
 
13.0%
mesozoic 286
 
4.5%
neoproterozoic 3
 
< 0.1%
mesoproterozoic 1
 
< 0.1%
2025-02-10T13:47:04.502411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 12698
24.6%
e 6349
12.3%
z 6345
12.3%
i 6345
12.3%
c 6345
12.3%
C 5229
10.1%
n 5229
10.1%
P 826
 
1.6%
a 826
 
1.6%
l 826
 
1.6%
Other values (6) 593
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 12698
24.6%
e 6349
12.3%
z 6345
12.3%
i 6345
12.3%
c 6345
12.3%
C 5229
10.1%
n 5229
10.1%
P 826
 
1.6%
a 826
 
1.6%
l 826
 
1.6%
Other values (6) 593
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 12698
24.6%
e 6349
12.3%
z 6345
12.3%
i 6345
12.3%
c 6345
12.3%
C 5229
10.1%
n 5229
10.1%
P 826
 
1.6%
a 826
 
1.6%
l 826
 
1.6%
Other values (6) 593
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 12698
24.6%
e 6349
12.3%
z 6345
12.3%
i 6345
12.3%
c 6345
12.3%
C 5229
10.1%
n 5229
10.1%
P 826
 
1.6%
a 826
 
1.6%
l 826
 
1.6%
Other values (6) 593
 
1.1%
Distinct27
Distinct (%)< 0.1%
Missing245750
Missing (%)33.9%
Memory size5.5 MiB
2025-02-10T13:47:04.538900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.607453035
Min length6

Characters and Unicode

Total characters4120887
Distinct characters35
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 rowTriassic
2nd rowPaleogene
3rd rowNeogene
4th rowPermian
5th rowQuaternary
ValueCountFrequency (%)
paleogene 90464
18.9%
neogene 72075
15.1%
cambrian 48808
10.2%
recent 41336
8.6%
ordovician 34462
 
7.2%
cretaceous 34238
 
7.2%
permian 32455
 
6.8%
quaternary 27798
 
5.8%
devonian 27637
 
5.8%
mississippian 19734
 
4.1%
Other values (14) 49751
10.4%
2025-02-10T13:47:04.637871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 751141
18.2%
n 506768
12.3%
a 458678
11.1%
i 322536
 
7.8%
o 263741
 
6.4%
r 242986
 
5.9%
g 162539
 
3.9%
s 160613
 
3.9%
P 140533
 
3.4%
c 124669
 
3.0%
Other values (25) 986683
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4120887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 751141
18.2%
n 506768
12.3%
a 458678
11.1%
i 322536
 
7.8%
o 263741
 
6.4%
r 242986
 
5.9%
g 162539
 
3.9%
s 160613
 
3.9%
P 140533
 
3.4%
c 124669
 
3.0%
Other values (25) 986683
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4120887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 751141
18.2%
n 506768
12.3%
a 458678
11.1%
i 322536
 
7.8%
o 263741
 
6.4%
r 242986
 
5.9%
g 162539
 
3.9%
s 160613
 
3.9%
P 140533
 
3.4%
c 124669
 
3.0%
Other values (25) 986683
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4120887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 751141
18.2%
n 506768
12.3%
a 458678
11.1%
i 322536
 
7.8%
o 263741
 
6.4%
r 242986
 
5.9%
g 162539
 
3.9%
s 160613
 
3.9%
P 140533
 
3.4%
c 124669
 
3.0%
Other values (25) 986683
23.9%
Distinct15
Distinct (%)0.2%
Missing718167
Missing (%)99.1%
Memory size5.5 MiB
2025-02-10T13:47:04.670059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.077905693
Min length6

Characters and Unicode

Total characters51222
Distinct characters28
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 rowDevonian
2nd rowNeogene
3rd rowCretaceous
4th rowQuaternary
5th rowRecent
ValueCountFrequency (%)
neogene 3161
49.9%
paleogene 1404
22.1%
quaternary 668
 
10.5%
devonian 416
 
6.6%
cretaceous 185
 
2.9%
cambrian 161
 
2.5%
ordovician 137
 
2.2%
pennsylvanian 77
 
1.2%
recent 60
 
0.9%
silurian 30
 
0.5%
Other values (5) 42
 
0.7%
2025-02-10T13:47:04.764085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15352
30.0%
n 6768
13.2%
o 5307
 
10.4%
g 4565
 
8.9%
a 4026
 
7.9%
N 3161
 
6.2%
r 1892
 
3.7%
l 1511
 
2.9%
P 1484
 
2.9%
i 1053
 
2.1%
Other values (18) 6103
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15352
30.0%
n 6768
13.2%
o 5307
 
10.4%
g 4565
 
8.9%
a 4026
 
7.9%
N 3161
 
6.2%
r 1892
 
3.7%
l 1511
 
2.9%
P 1484
 
2.9%
i 1053
 
2.1%
Other values (18) 6103
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15352
30.0%
n 6768
13.2%
o 5307
 
10.4%
g 4565
 
8.9%
a 4026
 
7.9%
N 3161
 
6.2%
r 1892
 
3.7%
l 1511
 
2.9%
P 1484
 
2.9%
i 1053
 
2.1%
Other values (18) 6103
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15352
30.0%
n 6768
13.2%
o 5307
 
10.4%
g 4565
 
8.9%
a 4026
 
7.9%
N 3161
 
6.2%
r 1892
 
3.7%
l 1511
 
2.9%
P 1484
 
2.9%
i 1053
 
2.1%
Other values (18) 6103
 
11.9%
Distinct24
Distinct (%)< 0.1%
Missing376914
Missing (%)52.0%
Memory size5.5 MiB
2025-02-10T13:47:04.801357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.357434248
Min length1

Characters and Unicode

Total characters2209806
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMiddle
2nd rowEocene
3rd rowPliocene
4th rowPleistocene
5th rowEarly
ValueCountFrequency (%)
middle 68576
19.7%
eocene 66980
19.3%
late 57993
16.7%
miocene 39410
11.3%
early 37474
10.8%
pliocene 32039
9.2%
pleistocene 20013
 
5.8%
oligocene 15521
 
4.5%
paleocene 7752
 
2.2%
holocene 1481
 
0.4%
Other values (10) 355
 
0.1%
2025-02-10T13:47:04.895592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 520801
23.6%
o 184703
 
8.4%
n 183525
 
8.3%
c 183200
 
8.3%
l 183151
 
8.3%
i 175926
 
8.0%
d 137364
 
6.2%
M 107985
 
4.9%
E 104453
 
4.7%
a 104017
 
4.7%
Other values (22) 324681
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2209806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 520801
23.6%
o 184703
 
8.4%
n 183525
 
8.3%
c 183200
 
8.3%
l 183151
 
8.3%
i 175926
 
8.0%
d 137364
 
6.2%
M 107985
 
4.9%
E 104453
 
4.7%
a 104017
 
4.7%
Other values (22) 324681
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2209806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 520801
23.6%
o 184703
 
8.4%
n 183525
 
8.3%
c 183200
 
8.3%
l 183151
 
8.3%
i 175926
 
8.0%
d 137364
 
6.2%
M 107985
 
4.9%
E 104453
 
4.7%
a 104017
 
4.7%
Other values (22) 324681
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2209806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 520801
23.6%
o 184703
 
8.4%
n 183525
 
8.3%
c 183200
 
8.3%
l 183151
 
8.3%
i 175926
 
8.0%
d 137364
 
6.2%
M 107985
 
4.9%
E 104453
 
4.7%
a 104017
 
4.7%
Other values (22) 324681
14.7%
Distinct12
Distinct (%)0.2%
Missing718290
Missing (%)99.1%
Memory size5.5 MiB
2025-02-10T13:47:04.931521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.33708588
Min length4

Characters and Unicode

Total characters45622
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMiddle
2nd rowPliocene
3rd rowLate
4th rowPleistocene
5th rowMiocene
ValueCountFrequency (%)
pliocene 2384
38.3%
eocene 1075
17.3%
miocene 759
 
12.2%
late 645
 
10.4%
pleistocene 645
 
10.4%
middle 364
 
5.9%
oligocene 188
 
3.0%
paleocene 97
 
1.6%
early 34
 
0.5%
holocene 14
 
0.2%
Other values (2) 13
 
0.2%
2025-02-10T13:47:05.026172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12099
26.5%
o 5177
11.3%
n 5176
11.3%
c 5174
11.3%
i 4342
 
9.5%
l 3726
 
8.2%
P 3126
 
6.9%
t 1302
 
2.9%
M 1123
 
2.5%
E 1109
 
2.4%
Other values (11) 3268
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12099
26.5%
o 5177
11.3%
n 5176
11.3%
c 5174
11.3%
i 4342
 
9.5%
l 3726
 
8.2%
P 3126
 
6.9%
t 1302
 
2.9%
M 1123
 
2.5%
E 1109
 
2.4%
Other values (11) 3268
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12099
26.5%
o 5177
11.3%
n 5176
11.3%
c 5174
11.3%
i 4342
 
9.5%
l 3726
 
8.2%
P 3126
 
6.9%
t 1302
 
2.9%
M 1123
 
2.5%
E 1109
 
2.4%
Other values (11) 3268
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12099
26.5%
o 5177
11.3%
n 5176
11.3%
c 5174
11.3%
i 4342
 
9.5%
l 3726
 
8.2%
P 3126
 
6.9%
t 1302
 
2.9%
M 1123
 
2.5%
E 1109
 
2.4%
Other values (11) 3268
 
7.2%
Distinct366
Distinct (%)0.2%
Missing562472
Missing (%)77.6%
Memory size5.5 MiB
2025-02-10T13:47:05.177461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.036053716
Min length4

Characters and Unicode

Total characters1464166
Distinct characters54
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

Unique38 ?
Unique (%)< 0.1%

Sample

1st rowAnisian
2nd rowHemphillian
3rd rowMiddle
4th rowEmsian
5th rowIrvingtonian
ValueCountFrequency (%)
hemphillian 19681
 
12.1%
middle 17380
 
10.7%
wasatchian 7037
 
4.3%
early 5466
 
3.4%
orellan 5085
 
3.1%
bridgerian 4799
 
2.9%
maastrichtian 4686
 
2.9%
campanian 4051
 
2.5%
chadronian 3871
 
2.4%
ypresian 3476
 
2.1%
Other values (350) 87399
53.6%
2025-02-10T13:47:05.405740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 228885
15.6%
n 195907
13.4%
i 190767
13.0%
e 105142
 
7.2%
l 96307
 
6.6%
r 75689
 
5.2%
d 61340
 
4.2%
o 52724
 
3.6%
h 47497
 
3.2%
s 40454
 
2.8%
Other values (44) 369454
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1464166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 228885
15.6%
n 195907
13.4%
i 190767
13.0%
e 105142
 
7.2%
l 96307
 
6.6%
r 75689
 
5.2%
d 61340
 
4.2%
o 52724
 
3.6%
h 47497
 
3.2%
s 40454
 
2.8%
Other values (44) 369454
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1464166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 228885
15.6%
n 195907
13.4%
i 190767
13.0%
e 105142
 
7.2%
l 96307
 
6.6%
r 75689
 
5.2%
d 61340
 
4.2%
o 52724
 
3.6%
h 47497
 
3.2%
s 40454
 
2.8%
Other values (44) 369454
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1464166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 228885
15.6%
n 195907
13.4%
i 190767
13.0%
e 105142
 
7.2%
l 96307
 
6.6%
r 75689
 
5.2%
d 61340
 
4.2%
o 52724
 
3.6%
h 47497
 
3.2%
s 40454
 
2.8%
Other values (44) 369454
25.2%
Distinct35
Distinct (%)1.5%
Missing722133
Missing (%)99.7%
Memory size5.5 MiB
2025-02-10T13:47:05.454511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.232
Min length4

Characters and Unicode

Total characters19551
Distinct characters38
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 (%)0.2%

Sample

1st rowGivetian
2nd rowTuronian
3rd rowGelasian
4th rowGelasian
5th rowGelasian
ValueCountFrequency (%)
lutetian 829
34.9%
zanclean 319
 
13.4%
tortonian 217
 
9.1%
gelasian 200
 
8.4%
maastrichtian 105
 
4.4%
late 98
 
4.1%
messinian 78
 
3.3%
thanetian 78
 
3.3%
ypresian 60
 
2.5%
langhian 58
 
2.4%
Other values (25) 333
14.0%
2025-02-10T13:47:05.552931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3358
17.2%
n 3107
15.9%
t 2287
11.7%
i 2268
11.6%
e 1838
9.4%
L 1015
 
5.2%
u 862
 
4.4%
l 662
 
3.4%
o 553
 
2.8%
s 534
 
2.7%
Other values (28) 3067
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3358
17.2%
n 3107
15.9%
t 2287
11.7%
i 2268
11.6%
e 1838
9.4%
L 1015
 
5.2%
u 862
 
4.4%
l 662
 
3.4%
o 553
 
2.8%
s 534
 
2.7%
Other values (28) 3067
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3358
17.2%
n 3107
15.9%
t 2287
11.7%
i 2268
11.6%
e 1838
9.4%
L 1015
 
5.2%
u 862
 
4.4%
l 662
 
3.4%
o 553
 
2.8%
s 534
 
2.7%
Other values (28) 3067
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3358
17.2%
n 3107
15.9%
t 2287
11.7%
i 2268
11.6%
e 1838
9.4%
L 1015
 
5.2%
u 862
 
4.4%
l 662
 
3.4%
o 553
 
2.8%
s 534
 
2.7%
Other values (28) 3067
15.7%

group
Text

Missing 

Distinct557
Distinct (%)0.6%
Missing633218
Missing (%)87.4%
Memory size5.5 MiB
2025-02-10T13:47:05.584651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length28
Mean length14.80891664
Min length1

Characters and Unicode

Total characters1351906
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

Unique146 ?
Unique (%)0.2%

Sample

1st rowStar Peak Group
2nd rowChesapeake Group
3rd rowKeokuk Group
4th rowChesapeake Group
5th rowChesapeake Group
ValueCountFrequency (%)
group 90331
46.7%
chesapeake 38410
19.9%
river 7802
 
4.0%
white 5751
 
3.0%
selma 3439
 
1.8%
kewanee 2702
 
1.4%
hamilton 2337
 
1.2%
osage 2256
 
1.2%
washita 1421
 
0.7%
pamunkey 1419
 
0.7%
Other values (577) 37508
19.4%
2025-02-10T13:47:05.680046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 166874
12.3%
p 131366
9.7%
a 118438
 
8.8%
r 115845
 
8.6%
o 113583
 
8.4%
102086
 
7.6%
u 98547
 
7.3%
G 90741
 
6.7%
s 54633
 
4.0%
h 50628
 
3.7%
Other values (47) 309165
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1351906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 166874
12.3%
p 131366
9.7%
a 118438
 
8.8%
r 115845
 
8.6%
o 113583
 
8.4%
102086
 
7.6%
u 98547
 
7.3%
G 90741
 
6.7%
s 54633
 
4.0%
h 50628
 
3.7%
Other values (47) 309165
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1351906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 166874
12.3%
p 131366
9.7%
a 118438
 
8.8%
r 115845
 
8.6%
o 113583
 
8.4%
102086
 
7.6%
u 98547
 
7.3%
G 90741
 
6.7%
s 54633
 
4.0%
h 50628
 
3.7%
Other values (47) 309165
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1351906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 166874
12.3%
p 131366
9.7%
a 118438
 
8.8%
r 115845
 
8.6%
o 113583
 
8.4%
102086
 
7.6%
u 98547
 
7.3%
G 90741
 
6.7%
s 54633
 
4.0%
h 50628
 
3.7%
Other values (47) 309165
22.9%

formation
Text

Missing 

Distinct5419
Distinct (%)1.5%
Missing365706
Missing (%)50.5%
Memory size5.5 MiB
2025-02-10T13:47:05.714186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length38
Mean length11.49027319
Min length3

Characters and Unicode

Total characters4122733
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

Unique1482 ?
Unique (%)0.4%

Sample

1st rowPrida Fm
2nd rowYorktown Fm
3rd rowSkinner Ranch Fm
4th rowSan Pedro Fm
5th rowGrande Greve Fm
ValueCountFrequency (%)
fm 259134
32.0%
river 44301
 
5.5%
ls 39737
 
4.9%
stephen 31376
 
3.9%
green 29207
 
3.6%
yorktown 23754
 
2.9%
unknown 18762
 
2.3%
sh 17735
 
2.2%
pungo 10262
 
1.3%
canyon 8111
 
1.0%
Other values (4425) 326422
40.4%
2025-02-10T13:47:05.813154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
449999
 
10.9%
e 361227
 
8.8%
n 317355
 
7.7%
m 288475
 
7.0%
F 271104
 
6.6%
r 245377
 
6.0%
o 238913
 
5.8%
a 212844
 
5.2%
i 166070
 
4.0%
t 160119
 
3.9%
Other values (56) 1411250
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4122733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
449999
 
10.9%
e 361227
 
8.8%
n 317355
 
7.7%
m 288475
 
7.0%
F 271104
 
6.6%
r 245377
 
6.0%
o 238913
 
5.8%
a 212844
 
5.2%
i 166070
 
4.0%
t 160119
 
3.9%
Other values (56) 1411250
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4122733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
449999
 
10.9%
e 361227
 
8.8%
n 317355
 
7.7%
m 288475
 
7.0%
F 271104
 
6.6%
r 245377
 
6.0%
o 238913
 
5.8%
a 212844
 
5.2%
i 166070
 
4.0%
t 160119
 
3.9%
Other values (56) 1411250
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4122733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
449999
 
10.9%
e 361227
 
8.8%
n 317355
 
7.7%
m 288475
 
7.0%
F 271104
 
6.6%
r 245377
 
6.0%
o 238913
 
5.8%
a 212844
 
5.2%
i 166070
 
4.0%
t 160119
 
3.9%
Other values (56) 1411250
34.2%

member
Text

Missing 

Distinct1626
Distinct (%)2.0%
Missing643191
Missing (%)88.8%
Memory size5.5 MiB
2025-02-10T13:47:05.845291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length30
Mean length13.99831524
Min length1

Characters and Unicode

Total characters1138301
Distinct characters70
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

Unique471 ?
Unique (%)0.6%

Sample

1st rowFossil Hill Mbr
2nd rowDecie Ranch Mbr
3rd rowMillersburg Mbr
4th rowThin-Bedded Zone Of Udden
5th rowBurgess Sh Mbr
ValueCountFrequency (%)
mbr 79698
34.1%
sh 36967
15.8%
burgess 30811
 
13.2%
ls 6535
 
2.8%
creek 4230
 
1.8%
sunken 3525
 
1.5%
meadow 3525
 
1.5%
ranch 3361
 
1.4%
francis 2603
 
1.1%
b 2492
 
1.1%
Other values (1500) 60135
25.7%
2025-02-10T13:47:05.945867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
152565
13.4%
r 138201
12.1%
M 87327
 
7.7%
s 86157
 
7.6%
b 84523
 
7.4%
e 79157
 
7.0%
h 47967
 
4.2%
S 46866
 
4.1%
u 42615
 
3.7%
a 41195
 
3.6%
Other values (60) 331728
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1138301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
152565
13.4%
r 138201
12.1%
M 87327
 
7.7%
s 86157
 
7.6%
b 84523
 
7.4%
e 79157
 
7.0%
h 47967
 
4.2%
S 46866
 
4.1%
u 42615
 
3.7%
a 41195
 
3.6%
Other values (60) 331728
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1138301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
152565
13.4%
r 138201
12.1%
M 87327
 
7.7%
s 86157
 
7.6%
b 84523
 
7.4%
e 79157
 
7.0%
h 47967
 
4.2%
S 46866
 
4.1%
u 42615
 
3.7%
a 41195
 
3.6%
Other values (60) 331728
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1138301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
152565
13.4%
r 138201
12.1%
M 87327
 
7.7%
s 86157
 
7.6%
b 84523
 
7.4%
e 79157
 
7.0%
h 47967
 
4.2%
S 46866
 
4.1%
u 42615
 
3.7%
a 41195
 
3.6%
Other values (60) 331728
29.1%

typeStatus
Text

Missing 

Distinct57
Distinct (%)< 0.1%
Missing581882
Missing (%)80.3%
Memory size5.5 MiB
2025-02-10T13:47:05.976848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length8
Mean length7.816414959
Min length4

Characters and Unicode

Total characters1114824
Distinct characters25
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

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowParatype
2nd rowParatype
3rd rowParatype
4th rowType
5th rowHolotype
ValueCountFrequency (%)
paratype 74620
52.2%
holotype 34727
24.3%
syntype 19596
 
13.7%
type 7957
 
5.6%
paralectotype 2999
 
2.1%
lectotype 1087
 
0.8%
plastoholotype 595
 
0.4%
plastotype 390
 
0.3%
plastoparatype 282
 
0.2%
plastosyntype 253
 
0.2%
Other values (12) 325
 
0.2%
2025-02-10T13:47:06.068899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 162651
14.6%
a 157416
14.1%
e 147041
13.2%
p 143090
12.8%
t 140517
12.6%
P 79203
7.1%
r 77963
7.0%
o 76542
6.9%
l 39911
 
3.6%
H 34727
 
3.1%
Other values (15) 55763
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1114824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 162651
14.6%
a 157416
14.1%
e 147041
13.2%
p 143090
12.8%
t 140517
12.6%
P 79203
7.1%
r 77963
7.0%
o 76542
6.9%
l 39911
 
3.6%
H 34727
 
3.1%
Other values (15) 55763
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1114824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 162651
14.6%
a 157416
14.1%
e 147041
13.2%
p 143090
12.8%
t 140517
12.6%
P 79203
7.1%
r 77963
7.0%
o 76542
6.9%
l 39911
 
3.6%
H 34727
 
3.1%
Other values (15) 55763
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1114824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 162651
14.6%
a 157416
14.1%
e 147041
13.2%
p 143090
12.8%
t 140517
12.6%
P 79203
7.1%
r 77963
7.0%
o 76542
6.9%
l 39911
 
3.6%
H 34727
 
3.1%
Other values (15) 55763
 
5.0%

identifiedBy
Text

Missing 

Distinct2463
Distinct (%)1.2%
Missing521981
Missing (%)72.0%
Memory size5.5 MiB
2025-02-10T13:47:06.220062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length147
Median length124
Mean length22.47668212
Min length2

Characters and Unicode

Total characters4552135
Distinct characters68
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

Unique535 ?
Unique (%)0.3%

Sample

1st rowSilberling; Nichols
2nd rowVaughan
3rd rowHarper; Boucot
4th rowSaid; Barakat, M. G.
5th rowSmith
ValueCountFrequency (%)
united 21468
 
3.2%
states 21082
 
3.2%
of 20281
 
3.1%
museum 15734
 
2.4%
helen 15316
 
2.3%
12006
 
1.8%
natural 11887
 
1.8%
history 11620
 
1.8%
institution 11572
 
1.7%
smithsonian 11571
 
1.7%
Other values (2466) 510240
77.0%
2025-02-10T13:47:06.456310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
460250
 
10.1%
e 280098
 
6.2%
o 272102
 
6.0%
a 259642
 
5.7%
n 241275
 
5.3%
t 230888
 
5.1%
r 226036
 
5.0%
i 214007
 
4.7%
l 181066
 
4.0%
s 174306
 
3.8%
Other values (58) 2012465
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4552135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
460250
 
10.1%
e 280098
 
6.2%
o 272102
 
6.0%
a 259642
 
5.7%
n 241275
 
5.3%
t 230888
 
5.1%
r 226036
 
5.0%
i 214007
 
4.7%
l 181066
 
4.0%
s 174306
 
3.8%
Other values (58) 2012465
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4552135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
460250
 
10.1%
e 280098
 
6.2%
o 272102
 
6.0%
a 259642
 
5.7%
n 241275
 
5.3%
t 230888
 
5.1%
r 226036
 
5.0%
i 214007
 
4.7%
l 181066
 
4.0%
s 174306
 
3.8%
Other values (58) 2012465
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4552135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
460250
 
10.1%
e 280098
 
6.2%
o 272102
 
6.0%
a 259642
 
5.7%
n 241275
 
5.3%
t 230888
 
5.1%
r 226036
 
5.0%
i 214007
 
4.7%
l 181066
 
4.0%
s 174306
 
3.8%
Other values (58) 2012465
44.2%

scientificName
Text

Missing 

Distinct97401
Distinct (%)17.6%
Missing171332
Missing (%)23.6%
Memory size5.5 MiB
2025-02-10T13:47:06.511586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length62
Median length56
Mean length18.07695742
Min length5

Characters and Unicode

Total characters9999739
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

Unique44766 ?
Unique (%)8.1%

Sample

1st rowDamaliscus lunatus
2nd rowAcrochordiceras hyatti
3rd rowDiscocyclina (Asterocyclina) sculpturata
4th rowOdontaspis cuspidata
5th rowEnteletes rotundobesus
ValueCountFrequency (%)
sp 136960
 
12.1%
genus 56232
 
5.0%
insecta 16851
 
1.5%
splendens 12400
 
1.1%
marrella 12281
 
1.1%
pterodroma 7305
 
0.6%
var 6498
 
0.6%
callophoca 3770
 
0.3%
isurus 3463
 
0.3%
ostracoda 3391
 
0.3%
Other values (53913) 873954
77.1%
2025-02-10T13:47:06.705651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1021294
 
10.2%
s 909134
 
9.1%
i 819278
 
8.2%
e 762530
 
7.6%
o 610330
 
6.1%
r 609311
 
6.1%
n 592254
 
5.9%
579929
 
5.8%
l 537519
 
5.4%
u 466436
 
4.7%
Other values (62) 3091724
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9999739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1021294
 
10.2%
s 909134
 
9.1%
i 819278
 
8.2%
e 762530
 
7.6%
o 610330
 
6.1%
r 609311
 
6.1%
n 592254
 
5.9%
579929
 
5.8%
l 537519
 
5.4%
u 466436
 
4.7%
Other values (62) 3091724
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9999739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1021294
 
10.2%
s 909134
 
9.1%
i 819278
 
8.2%
e 762530
 
7.6%
o 610330
 
6.1%
r 609311
 
6.1%
n 592254
 
5.9%
579929
 
5.8%
l 537519
 
5.4%
u 466436
 
4.7%
Other values (62) 3091724
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9999739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1021294
 
10.2%
s 909134
 
9.1%
i 819278
 
8.2%
e 762530
 
7.6%
o 610330
 
6.1%
r 609311
 
6.1%
n 592254
 
5.9%
579929
 
5.8%
l 537519
 
5.4%
u 466436
 
4.7%
Other values (62) 3091724
30.9%

higherClassification
Text

Missing 

Distinct3844
Distinct (%)0.7%
Missing172643
Missing (%)23.8%
Memory size5.5 MiB
2025-02-10T13:47:06.863217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length141
Median length123
Mean length59.08444638
Min length5

Characters and Unicode

Total characters32606638
Distinct characters61
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

Unique743 ?
Unique (%)0.1%

Sample

1st rowAnimalia, Chordata, Vertebrata, Mammalia, Eutheria, Laurasiatheria, Artiodactyla, Ruminatia, Bovidae
2nd rowAnimalia, Mollusca, Cephalopoda, Ammonoidea
3rd rowChromista, Foraminifera, Globothalamea, Rotaliida, Discocyclinidae
4th rowAnimalia, Chordata, Vertebrata, Pisces, Chondrichthyes, Elasmobranchii, Galeomorphii, Lamniformes, Odontaspididae
5th rowAnimalia, Brachiopoda, Rhynchonellata, Orthida, Enteletidae
ValueCountFrequency (%)
animalia 448323
 
15.7%
chordata 148700
 
5.2%
vertebrata 148618
 
5.2%
arthropoda 100318
 
3.5%
mollusca 69025
 
2.4%
brachiopoda 66748
 
2.3%
foraminifera 66301
 
2.3%
chromista 65999
 
2.3%
mammalia 60027
 
2.1%
eutheria 57586
 
2.0%
Other values (3834) 1620986
56.8%
2025-02-10T13:47:07.104597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4706865
14.4%
i 3184420
 
9.8%
2300766
 
7.1%
, 2260526
 
6.9%
o 2052009
 
6.3%
r 2005114
 
6.1%
e 1809015
 
5.5%
t 1671086
 
5.1%
l 1501858
 
4.6%
n 1400746
 
4.3%
Other values (51) 9714233
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32606638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4706865
14.4%
i 3184420
 
9.8%
2300766
 
7.1%
, 2260526
 
6.9%
o 2052009
 
6.3%
r 2005114
 
6.1%
e 1809015
 
5.5%
t 1671086
 
5.1%
l 1501858
 
4.6%
n 1400746
 
4.3%
Other values (51) 9714233
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32606638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4706865
14.4%
i 3184420
 
9.8%
2300766
 
7.1%
, 2260526
 
6.9%
o 2052009
 
6.3%
r 2005114
 
6.1%
e 1809015
 
5.5%
t 1671086
 
5.1%
l 1501858
 
4.6%
n 1400746
 
4.3%
Other values (51) 9714233
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32606638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4706865
14.4%
i 3184420
 
9.8%
2300766
 
7.1%
, 2260526
 
6.9%
o 2052009
 
6.3%
r 2005114
 
6.1%
e 1809015
 
5.5%
t 1671086
 
5.1%
l 1501858
 
4.6%
n 1400746
 
4.3%
Other values (51) 9714233
29.8%

kingdom
Text

Missing 

Distinct9
Distinct (%)< 0.1%
Missing172847
Missing (%)23.9%
Memory size5.5 MiB
2025-02-10T13:47:07.141328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.052434375
Min length5

Characters and Unicode

Total characters4442214
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowChromista
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 448322
81.3%
chromista 65985
 
12.0%
plantae 37205
 
6.7%
protoctista 66
 
< 0.1%
protozoa 44
 
< 0.1%
biota 28
 
< 0.1%
incertae 5
 
< 0.1%
sedis 5
 
< 0.1%
bacteria 5
 
< 0.1%
arthropoda 1
 
< 0.1%
2025-02-10T13:47:07.222210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1037193
23.3%
i 962733
21.7%
m 514307
11.6%
n 485532
10.9%
l 485527
10.9%
A 448323
10.1%
t 103471
 
2.3%
o 66279
 
1.5%
r 66107
 
1.5%
s 66061
 
1.5%
Other values (11) 206681
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4442214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1037193
23.3%
i 962733
21.7%
m 514307
11.6%
n 485532
10.9%
l 485527
10.9%
A 448323
10.1%
t 103471
 
2.3%
o 66279
 
1.5%
r 66107
 
1.5%
s 66061
 
1.5%
Other values (11) 206681
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4442214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1037193
23.3%
i 962733
21.7%
m 514307
11.6%
n 485532
10.9%
l 485527
10.9%
A 448323
10.1%
t 103471
 
2.3%
o 66279
 
1.5%
r 66107
 
1.5%
s 66061
 
1.5%
Other values (11) 206681
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4442214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1037193
23.3%
i 962733
21.7%
m 514307
11.6%
n 485532
10.9%
l 485527
10.9%
A 448323
10.1%
t 103471
 
2.3%
o 66279
 
1.5%
r 66107
 
1.5%
s 66061
 
1.5%
Other values (11) 206681
 
4.7%

phylum
Text

Missing 

Distinct34
Distinct (%)< 0.1%
Missing211856
Missing (%)29.2%
Memory size5.5 MiB
2025-02-10T13:47:07.255915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.567853047
Min length5

Characters and Unicode

Total characters4904979
Distinct characters34
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 rowChordata
2nd rowMollusca
3rd rowForaminifera
4th rowChordata
5th rowBrachiopoda
ValueCountFrequency (%)
chordata 148700
29.0%
arthropoda 100304
19.5%
mollusca 69025
13.4%
brachiopoda 66748
13.0%
foraminifera 65986
12.9%
echinodermata 26599
 
5.2%
bryozoa 12874
 
2.5%
cnidaria 7243
 
1.4%
protozoa 4080
 
0.8%
porifera 2897
 
0.6%
Other values (27) 8947
 
1.7%
2025-02-10T13:47:07.346737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 832296
17.0%
o 688644
14.0%
r 609931
12.4%
d 357317
 
7.3%
h 344816
 
7.0%
t 283255
 
5.8%
i 252208
 
5.1%
p 168801
 
3.4%
c 165860
 
3.4%
C 156159
 
3.2%
Other values (24) 1045692
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4904979
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 832296
17.0%
o 688644
14.0%
r 609931
12.4%
d 357317
 
7.3%
h 344816
 
7.0%
t 283255
 
5.8%
i 252208
 
5.1%
p 168801
 
3.4%
c 165860
 
3.4%
C 156159
 
3.2%
Other values (24) 1045692
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4904979
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 832296
17.0%
o 688644
14.0%
r 609931
12.4%
d 357317
 
7.3%
h 344816
 
7.0%
t 283255
 
5.8%
i 252208
 
5.1%
p 168801
 
3.4%
c 165860
 
3.4%
C 156159
 
3.2%
Other values (24) 1045692
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4904979
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 832296
17.0%
o 688644
14.0%
r 609931
12.4%
d 357317
 
7.3%
h 344816
 
7.0%
t 283255
 
5.8%
i 252208
 
5.1%
p 168801
 
3.4%
c 165860
 
3.4%
C 156159
 
3.2%
Other values (24) 1045692
21.3%

class
Text

Missing 

Distinct145
Distinct (%)< 0.1%
Missing235611
Missing (%)32.5%
Memory size5.5 MiB
2025-02-10T13:47:07.390694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length19
Mean length9.967651673
Min length4

Characters and Unicode

Total characters4873155
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowMammalia
2nd rowCephalopoda
3rd rowGlobothalamea
4th rowChondrichthyes
5th rowRhynchonellata
ValueCountFrequency (%)
mammalia 60027
 
12.2%
globothalamea 41779
 
8.5%
rhynchonellata 39023
 
7.9%
aves 34583
 
7.0%
insecta 29284
 
6.0%
chondrichthyes 26607
 
5.4%
gastropoda 24466
 
5.0%
ostracoda 24047
 
4.9%
trilobita 22871
 
4.7%
bivalvia 22291
 
4.5%
Other values (133) 165921
33.8%
2025-02-10T13:47:07.498780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 859113
17.6%
o 453975
 
9.3%
t 367169
 
7.5%
l 337501
 
6.9%
i 301652
 
6.2%
e 293993
 
6.0%
h 287732
 
5.9%
n 212707
 
4.4%
s 207229
 
4.3%
m 199854
 
4.1%
Other values (39) 1352230
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4873155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 859113
17.6%
o 453975
 
9.3%
t 367169
 
7.5%
l 337501
 
6.9%
i 301652
 
6.2%
e 293993
 
6.0%
h 287732
 
5.9%
n 212707
 
4.4%
s 207229
 
4.3%
m 199854
 
4.1%
Other values (39) 1352230
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4873155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 859113
17.6%
o 453975
 
9.3%
t 367169
 
7.5%
l 337501
 
6.9%
i 301652
 
6.2%
e 293993
 
6.0%
h 287732
 
5.9%
n 212707
 
4.4%
s 207229
 
4.3%
m 199854
 
4.1%
Other values (39) 1352230
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4873155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 859113
17.6%
o 453975
 
9.3%
t 367169
 
7.5%
l 337501
 
6.9%
i 301652
 
6.2%
e 293993
 
6.0%
h 287732
 
5.9%
n 212707
 
4.4%
s 207229
 
4.3%
m 199854
 
4.1%
Other values (39) 1352230
27.7%

order
Text

Missing 

Distinct552
Distinct (%)0.2%
Missing400004
Missing (%)55.2%
Memory size5.5 MiB
2025-02-10T13:47:07.654728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length22
Mean length11.13181656
Min length1

Characters and Unicode

Total characters3612319
Distinct characters54
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

Unique66 ?
Unique (%)< 0.1%

Sample

1st rowArtiodactyla
2nd rowAmmonoidea
3rd rowRotaliida
4th rowLamniformes
5th rowOrthida
ValueCountFrequency (%)
rotaliida 32318
 
9.7%
lamniformes 12411
 
3.7%
spiriferida 11138
 
3.3%
cetacea 10502
 
3.1%
productida 10020
 
3.0%
procellariiformes 9895
 
3.0%
ammonoidea 9257
 
2.8%
order 9090
 
2.7%
artiodactyla 8886
 
2.7%
terebratulida 8672
 
2.6%
Other values (536) 212022
63.4%
2025-02-10T13:47:07.882746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 454969
12.6%
a 442612
12.3%
r 320973
 
8.9%
o 301998
 
8.4%
e 264934
 
7.3%
d 249362
 
6.9%
t 203578
 
5.6%
l 161146
 
4.5%
s 140573
 
3.9%
n 136028
 
3.8%
Other values (44) 936146
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3612319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 454969
12.6%
a 442612
12.3%
r 320973
 
8.9%
o 301998
 
8.4%
e 264934
 
7.3%
d 249362
 
6.9%
t 203578
 
5.6%
l 161146
 
4.5%
s 140573
 
3.9%
n 136028
 
3.8%
Other values (44) 936146
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3612319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 454969
12.6%
a 442612
12.3%
r 320973
 
8.9%
o 301998
 
8.4%
e 264934
 
7.3%
d 249362
 
6.9%
t 203578
 
5.6%
l 161146
 
4.5%
s 140573
 
3.9%
n 136028
 
3.8%
Other values (44) 936146
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3612319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 454969
12.6%
a 442612
12.3%
r 320973
 
8.9%
o 301998
 
8.4%
e 264934
 
7.3%
d 249362
 
6.9%
t 203578
 
5.6%
l 161146
 
4.5%
s 140573
 
3.9%
n 136028
 
3.8%
Other values (44) 936146
25.9%

family
Text

Missing 

Distinct2441
Distinct (%)0.8%
Missing409455
Missing (%)56.5%
Memory size5.5 MiB
2025-02-10T13:47:08.050548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length23
Mean length12.35823496
Min length1

Characters and Unicode

Total characters3893499
Distinct characters60
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

Unique406 ?
Unique (%)0.1%

Sample

1st rowBovidae
2nd rowDiscocyclinidae
3rd rowOdontaspididae
4th rowEnteletidae
5th rowProcellariidae
ValueCountFrequency (%)
family 24920
 
7.3%
indet 24361
 
7.2%
procellariidae 9409
 
2.8%
carcharhinidae 6802
 
2.0%
lamnidae 6398
 
1.9%
anatidae 5246
 
1.5%
equidae 4518
 
1.3%
phocidae 4479
 
1.3%
odontaspididae 3901
 
1.1%
vaginulinidae 3658
 
1.1%
Other values (2428) 246880
72.5%
2025-02-10T13:47:08.277757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 562017
14.4%
e 500496
12.9%
a 474982
12.2%
d 376670
9.7%
o 212006
 
5.4%
l 211977
 
5.4%
r 188973
 
4.9%
n 186459
 
4.8%
t 179603
 
4.6%
c 107527
 
2.8%
Other values (50) 892789
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3893499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 562017
14.4%
e 500496
12.9%
a 474982
12.2%
d 376670
9.7%
o 212006
 
5.4%
l 211977
 
5.4%
r 188973
 
4.9%
n 186459
 
4.8%
t 179603
 
4.6%
c 107527
 
2.8%
Other values (50) 892789
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3893499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 562017
14.4%
e 500496
12.9%
a 474982
12.2%
d 376670
9.7%
o 212006
 
5.4%
l 211977
 
5.4%
r 188973
 
4.9%
n 186459
 
4.8%
t 179603
 
4.6%
c 107527
 
2.8%
Other values (50) 892789
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3893499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 562017
14.4%
e 500496
12.9%
a 474982
12.2%
d 376670
9.7%
o 212006
 
5.4%
l 211977
 
5.4%
r 188973
 
4.9%
n 186459
 
4.8%
t 179603
 
4.6%
c 107527
 
2.8%
Other values (50) 892789
22.9%

genus
Text

Missing 

Distinct20259
Distinct (%)3.8%
Missing197061
Missing (%)27.2%
Memory size5.5 MiB
2025-02-10T13:47:08.440588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length23
Mean length9.623302436
Min length1

Characters and Unicode

Total characters5075782
Distinct characters58
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

Unique5010 ?
Unique (%)0.9%

Sample

1st rowDamaliscus
2nd rowAcrochordiceras
3rd rowDiscocyclina
4th rowOdontaspis
5th rowEnteletes
ValueCountFrequency (%)
genus 56245
 
10.6%
marrella 12281
 
2.3%
pterodroma 7305
 
1.4%
callophoca 3770
 
0.7%
isurus 3463
 
0.7%
physeterula 3029
 
0.6%
carcharhinus 2930
 
0.6%
australca 2250
 
0.4%
thambetochen 2208
 
0.4%
hustedia 2082
 
0.4%
Other values (20248) 432660
81.9%
2025-02-10T13:47:08.670974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 526234
 
10.4%
e 421801
 
8.3%
i 409475
 
8.1%
o 392073
 
7.7%
s 365990
 
7.2%
r 360745
 
7.1%
l 312289
 
6.2%
n 296798
 
5.8%
u 263865
 
5.2%
t 240334
 
4.7%
Other values (48) 1486178
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5075782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 526234
 
10.4%
e 421801
 
8.3%
i 409475
 
8.1%
o 392073
 
7.7%
s 365990
 
7.2%
r 360745
 
7.1%
l 312289
 
6.2%
n 296798
 
5.8%
u 263865
 
5.2%
t 240334
 
4.7%
Other values (48) 1486178
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5075782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 526234
 
10.4%
e 421801
 
8.3%
i 409475
 
8.1%
o 392073
 
7.7%
s 365990
 
7.2%
r 360745
 
7.1%
l 312289
 
6.2%
n 296798
 
5.8%
u 263865
 
5.2%
t 240334
 
4.7%
Other values (48) 1486178
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5075782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 526234
 
10.4%
e 421801
 
8.3%
i 409475
 
8.1%
o 392073
 
7.7%
s 365990
 
7.2%
r 360745
 
7.1%
l 312289
 
6.2%
n 296798
 
5.8%
u 263865
 
5.2%
t 240334
 
4.7%
Other values (48) 1486178
29.3%

subgenus
Text

Missing 

Distinct2470
Distinct (%)11.1%
Missing702202
Missing (%)96.9%
Memory size5.5 MiB
2025-02-10T13:47:08.831704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.61570878
Min length3

Characters and Unicode

Total characters236794
Distinct characters58
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

Unique735 ?
Unique (%)3.3%

Sample

1st rowAsterocyclina
2nd rowRadiatrypa
3rd rowLaevidentalium
4th rowVacoea
5th rowPhyllonotus
ValueCountFrequency (%)
nephrolepidina 547
 
2.5%
lingulella 440
 
2.0%
lingulepis 430
 
1.9%
lepidocyclina 379
 
1.7%
dyoros 329
 
1.5%
eulepidina 285
 
1.3%
discocyclina 264
 
1.2%
vacoea 243
 
1.1%
chlamys 239
 
1.1%
proporocyclina 214
 
1.0%
Other values (2461) 18944
84.9%
2025-02-10T13:47:09.062906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 25775
 
10.9%
i 22604
 
9.5%
o 18830
 
8.0%
e 18657
 
7.9%
r 16116
 
6.8%
l 16112
 
6.8%
s 14304
 
6.0%
c 11983
 
5.1%
t 11285
 
4.8%
n 11277
 
4.8%
Other values (48) 69851
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 236794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 25775
 
10.9%
i 22604
 
9.5%
o 18830
 
8.0%
e 18657
 
7.9%
r 16116
 
6.8%
l 16112
 
6.8%
s 14304
 
6.0%
c 11983
 
5.1%
t 11285
 
4.8%
n 11277
 
4.8%
Other values (48) 69851
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 236794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 25775
 
10.9%
i 22604
 
9.5%
o 18830
 
8.0%
e 18657
 
7.9%
r 16116
 
6.8%
l 16112
 
6.8%
s 14304
 
6.0%
c 11983
 
5.1%
t 11285
 
4.8%
n 11277
 
4.8%
Other values (48) 69851
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 236794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 25775
 
10.9%
i 22604
 
9.5%
o 18830
 
8.0%
e 18657
 
7.9%
r 16116
 
6.8%
l 16112
 
6.8%
s 14304
 
6.0%
c 11983
 
5.1%
t 11285
 
4.8%
n 11277
 
4.8%
Other values (48) 69851
29.5%

specificEpithet
Text

Missing 

Distinct32184
Distinct (%)6.1%
Missing197674
Missing (%)27.3%
Memory size5.5 MiB
2025-02-10T13:47:09.110258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length21
Mean length7.031748141
Min length1

Characters and Unicode

Total characters3704564
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

Unique10223 ?
Unique (%)1.9%

Sample

1st rowlunatus
2nd rowhyatti
3rd rowsculpturata
4th rowcuspidata
5th rowrotundobesus
ValueCountFrequency (%)
sp 136976
 
25.7%
splendens 12400
 
2.3%
phaeopygia 3232
 
0.6%
species 2814
 
0.5%
a 2244
 
0.4%
bella 2150
 
0.4%
alba 2016
 
0.4%
megalodon 1645
 
0.3%
confluens 1466
 
0.3%
obscura 1275
 
0.2%
Other values (32112) 367401
68.9%
2025-02-10T13:47:09.215196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 492867
13.3%
a 409545
11.1%
i 366458
9.9%
e 293309
 
7.9%
n 257096
 
6.9%
p 241847
 
6.5%
r 211113
 
5.7%
l 197470
 
5.3%
u 185989
 
5.0%
o 183662
 
5.0%
Other values (34) 865208
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3704564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 492867
13.3%
a 409545
11.1%
i 366458
9.9%
e 293309
 
7.9%
n 257096
 
6.9%
p 241847
 
6.5%
r 211113
 
5.7%
l 197470
 
5.3%
u 185989
 
5.0%
o 183662
 
5.0%
Other values (34) 865208
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3704564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 492867
13.3%
a 409545
11.1%
i 366458
9.9%
e 293309
 
7.9%
n 257096
 
6.9%
p 241847
 
6.5%
r 211113
 
5.7%
l 197470
 
5.3%
u 185989
 
5.0%
o 183662
 
5.0%
Other values (34) 865208
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3704564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 492867
13.3%
a 409545
11.1%
i 366458
9.9%
e 293309
 
7.9%
n 257096
 
6.9%
p 241847
 
6.5%
r 211113
 
5.7%
l 197470
 
5.3%
u 185989
 
5.0%
o 183662
 
5.0%
Other values (34) 865208
23.4%

infraspecificEpithet
Text

Missing 

Distinct3295
Distinct (%)20.0%
Missing708037
Missing (%)97.7%
Memory size5.5 MiB
2025-02-10T13:47:09.364599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length8.558557465
Min length1

Characters and Unicode

Total characters140968
Distinct characters47
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

Unique1244 ?
Unique (%)7.6%

Sample

1st rowamplexoides
2nd rowgrandis
3rd rowcanalis
4th rowcooperensis
5th rowpyramidale
ValueCountFrequency (%)
burchelli 494
 
3.0%
halli 243
 
1.5%
a 159
 
1.0%
pugilla 151
 
0.9%
spinifera 136
 
0.8%
b 135
 
0.8%
antarctica 104
 
0.6%
bellaplicata 81
 
0.5%
nasiterna 79
 
0.5%
minor 78
 
0.5%
Other values (3272) 14872
90.0%
2025-02-10T13:47:09.587765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18791
13.3%
i 14907
10.6%
s 13226
9.4%
e 11648
 
8.3%
n 10012
 
7.1%
t 8967
 
6.4%
r 8880
 
6.3%
l 8863
 
6.3%
u 7809
 
5.5%
o 7067
 
5.0%
Other values (37) 30798
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 18791
13.3%
i 14907
10.6%
s 13226
9.4%
e 11648
 
8.3%
n 10012
 
7.1%
t 8967
 
6.4%
r 8880
 
6.3%
l 8863
 
6.3%
u 7809
 
5.5%
o 7067
 
5.0%
Other values (37) 30798
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 18791
13.3%
i 14907
10.6%
s 13226
9.4%
e 11648
 
8.3%
n 10012
 
7.1%
t 8967
 
6.4%
r 8880
 
6.3%
l 8863
 
6.3%
u 7809
 
5.5%
o 7067
 
5.0%
Other values (37) 30798
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 18791
13.3%
i 14907
10.6%
s 13226
9.4%
e 11648
 
8.3%
n 10012
 
7.1%
t 8967
 
6.4%
r 8880
 
6.3%
l 8863
 
6.3%
u 7809
 
5.5%
o 7067
 
5.0%
Other values (37) 30798
21.8%

taxonRank
Text

Missing 

Distinct5
Distinct (%)< 0.1%
Missing707802
Missing (%)97.7%
Memory size5.5 MiB
2025-02-10T13:47:09.623511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.738058183
Min length5

Characters and Unicode

Total characters145978
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsubspecies
2nd rowvariety
3rd rowsubspecies
4th rowvariety
5th rowsubspecies
ValueCountFrequency (%)
subspecies 9791
58.6%
variety 6728
40.3%
forma 134
 
0.8%
morpha 37
 
0.2%
clade 16
 
0.1%
2025-02-10T13:47:09.707064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 29373
20.1%
e 26326
18.0%
i 16519
11.3%
p 9828
 
6.7%
b 9791
 
6.7%
c 9791
 
6.7%
u 9791
 
6.7%
a 6915
 
4.7%
r 6899
 
4.7%
v 6728
 
4.6%
Other values (9) 14017
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 29373
20.1%
e 26326
18.0%
i 16519
11.3%
p 9828
 
6.7%
b 9791
 
6.7%
c 9791
 
6.7%
u 9791
 
6.7%
a 6915
 
4.7%
r 6899
 
4.7%
v 6728
 
4.6%
Other values (9) 14017
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 29373
20.1%
e 26326
18.0%
i 16519
11.3%
p 9828
 
6.7%
b 9791
 
6.7%
c 9791
 
6.7%
u 9791
 
6.7%
a 6915
 
4.7%
r 6899
 
4.7%
v 6728
 
4.6%
Other values (9) 14017
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 29373
20.1%
e 26326
18.0%
i 16519
11.3%
p 9828
 
6.7%
b 9791
 
6.7%
c 9791
 
6.7%
u 9791
 
6.7%
a 6915
 
4.7%
r 6899
 
4.7%
v 6728
 
4.6%
Other values (9) 14017
9.6%
Distinct7319
Distinct (%)1.8%
Missing325030
Missing (%)44.9%
Memory size5.5 MiB
2025-02-10T13:47:09.744474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length103
Median length51
Mean length9.144288296
Min length2

Characters and Unicode

Total characters3652942
Distinct characters76
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

Unique1579 ?
Unique (%)0.4%

Sample

1st rowMeek
2nd row(Cushman)
3rd row(Agassiz)
4th rowCooper & Grant
5th rowCuvier
ValueCountFrequency (%)
77310
 
13.1%
walcott 26311
 
4.5%
cooper 26282
 
4.4%
cushman 17375
 
2.9%
grant 16892
 
2.9%
ulrich 12249
 
2.1%
et 9463
 
1.6%
al 9463
 
1.6%
hall 8176
 
1.4%
bassler 5943
 
1.0%
Other values (4208) 381568
64.6%
2025-02-10T13:47:09.923971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 302103
 
8.3%
a 256596
 
7.0%
o 243833
 
6.7%
r 239853
 
6.6%
n 225453
 
6.2%
l 204010
 
5.6%
191554
 
5.2%
t 170449
 
4.7%
i 153159
 
4.2%
s 150944
 
4.1%
Other values (66) 1514988
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3652942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 302103
 
8.3%
a 256596
 
7.0%
o 243833
 
6.7%
r 239853
 
6.6%
n 225453
 
6.2%
l 204010
 
5.6%
191554
 
5.2%
t 170449
 
4.7%
i 153159
 
4.2%
s 150944
 
4.1%
Other values (66) 1514988
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3652942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 302103
 
8.3%
a 256596
 
7.0%
o 243833
 
6.7%
r 239853
 
6.6%
n 225453
 
6.2%
l 204010
 
5.6%
191554
 
5.2%
t 170449
 
4.7%
i 153159
 
4.2%
s 150944
 
4.1%
Other values (66) 1514988
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3652942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 302103
 
8.3%
a 256596
 
7.0%
o 243833
 
6.7%
r 239853
 
6.6%
n 225453
 
6.2%
l 204010
 
5.6%
191554
 
5.2%
t 170449
 
4.7%
i 153159
 
4.2%
s 150944
 
4.1%
Other values (66) 1514988
41.5%