Overview

Brought to you by YData

Dataset statistics

Number of variables112
Number of observations4515661
Missing cells350029119
Missing cells (%)69.2%
Total size in memory3.8 GiB
Average record size in memory896.0 B

Variable types

Text112

Dataset

DescriptionUS NMNH Extant Specimen Records 0052487-241126133413365
URLhttps://doi.org/10.15468/hnhrg3

Alerts

institutionID has constant value "urn:lsid:biocol.org:col:15463" Constant
collectionID has constant value "urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8" Constant
institutionCode has constant value "US" Constant
collectionCode has constant value "US" Constant
datasetName has constant value "NMNH Extant Biology" Constant
eventType has constant value "-7.38" Constant
samplingProtocol has constant value "400.0" Constant
countryCode has constant value "1872" Constant
verbatimSRS has constant value "San Francisco" Constant
lithostratigraphicTerms has constant value "500.0" Constant
bed has constant value "Riccardia pinguis" Constant
identificationID has constant value "Variety" Constant
taxonID has constant value "Metzgeriales" Constant
acceptedNameUsageID has constant value "Aneuraceae" Constant
namePublishedInID has constant value "Riccardia" Constant
nameAccordingTo has constant value "variety" Constant
nomenclaturalCode has constant value "Skog, Laurence E." Constant
taxonRemarks has constant value "Plantae" Constant
catalogNumber has 604654 (13.4%) missing values Missing
recordedBy has 54378 (1.2%) missing values Missing
lifeStage has 4152582 (92.0%) missing values Missing
preparations has 4381212 (97.0%) missing values Missing
associatedMedia has 318147 (7.0%) missing values Missing
associatedSequences has 4515310 (> 99.9%) missing values Missing
occurrenceRemarks has 4424129 (98.0%) missing values Missing
organismName has 4515658 (> 99.9%) missing values Missing
eventType has 4515660 (> 99.9%) missing values Missing
fieldNumber has 4515399 (> 99.9%) missing values Missing
eventDate has 499507 (11.1%) missing values Missing
startDayOfYear has 707937 (15.7%) missing values Missing
endDayOfYear has 706297 (15.6%) missing values Missing
year has 499507 (11.1%) missing values Missing
month has 700051 (15.5%) missing values Missing
day has 1180026 (26.1%) missing values Missing
verbatimEventDate has 2995056 (66.3%) missing values Missing
habitat has 4009333 (88.8%) missing values Missing
samplingProtocol has 4515660 (> 99.9%) missing values Missing
sampleSizeValue has 4515659 (> 99.9%) missing values Missing
locationID has 4473993 (99.1%) missing values Missing
continent has 66158 (1.5%) missing values Missing
waterBody has 4496088 (99.6%) missing values Missing
islandGroup has 4403077 (97.5%) missing values Missing
island has 4139168 (91.7%) missing values Missing
countryCode has 4515660 (> 99.9%) missing values Missing
stateProvince has 1002183 (22.2%) missing values Missing
county has 3778676 (83.7%) missing values Missing
locality has 332028 (7.4%) missing values Missing
verbatimLocality has 4515656 (> 99.9%) missing values Missing
minimumElevationInMeters has 2860984 (63.4%) missing values Missing
maximumElevationInMeters has 4017410 (89.0%) missing values Missing
minimumDepthInMeters has 4475632 (99.1%) missing values Missing
maximumDepthInMeters has 4478965 (99.2%) missing values Missing
verbatimDepth has 4494022 (99.5%) missing values Missing
decimalLatitude has 3845453 (85.2%) missing values Missing
decimalLongitude has 3845454 (85.2%) missing values Missing
geodeticDatum has 4485859 (99.3%) missing values Missing
coordinateUncertaintyInMeters has 4509192 (99.9%) missing values Missing
coordinatePrecision has 4515658 (> 99.9%) missing values Missing
pointRadiusSpatialFit has 4515656 (> 99.9%) missing values Missing
verbatimCoordinates has 4515657 (> 99.9%) missing values Missing
verbatimLatitude has 4477670 (99.2%) missing values Missing
verbatimLongitude has 4477686 (99.2%) missing values Missing
verbatimCoordinateSystem has 4478628 (99.2%) missing values Missing
verbatimSRS has 4515660 (> 99.9%) missing values Missing
footprintSpatialFit has 4515658 (> 99.9%) missing values Missing
georeferenceProtocol has 4388537 (97.2%) missing values Missing
georeferenceRemarks has 4515150 (> 99.9%) missing values Missing
geologicalContextID has 4515657 (> 99.9%) missing values Missing
earliestEonOrLowestEonothem has 4515654 (> 99.9%) missing values Missing
latestEonOrHighestEonothem has 4515654 (> 99.9%) missing values Missing
latestEraOrHighestErathem has 4515658 (> 99.9%) missing values Missing
earliestPeriodOrLowestSystem has 4515654 (> 99.9%) missing values Missing
earliestEpochOrLowestSeries has 4515654 (> 99.9%) missing values Missing
latestEpochOrHighestSeries has 4515659 (> 99.9%) missing values Missing
latestAgeOrHighestStage has 4515657 (> 99.9%) missing values Missing
lowestBiostratigraphicZone has 4515658 (> 99.9%) missing values Missing
highestBiostratigraphicZone has 4515658 (> 99.9%) missing values Missing
lithostratigraphicTerms has 4515660 (> 99.9%) missing values Missing
formation has 4515658 (> 99.9%) missing values Missing
member has 4515654 (> 99.9%) missing values Missing
bed has 4515660 (> 99.9%) missing values Missing
identificationID has 4515660 (> 99.9%) missing values Missing
identificationQualifier has 4504655 (99.8%) missing values Missing
typeStatus has 4399315 (97.4%) missing values Missing
identifiedBy has 3958097 (87.7%) missing values Missing
identifiedByID has 4515655 (> 99.9%) missing values Missing
dateIdentified has 4515654 (> 99.9%) missing values Missing
identificationReferences has 4515655 (> 99.9%) missing values Missing
identificationVerificationStatus has 4515654 (> 99.9%) missing values Missing
identificationRemarks has 4515654 (> 99.9%) missing values Missing
taxonID has 4515660 (> 99.9%) missing values Missing
scientificNameID has 4515655 (> 99.9%) missing values Missing
acceptedNameUsageID has 4515660 (> 99.9%) missing values Missing
nameAccordingToID has 4515655 (> 99.9%) missing values Missing
namePublishedInID has 4515660 (> 99.9%) missing values Missing
acceptedNameUsage has 4515655 (> 99.9%) missing values Missing
parentNameUsage has 4515659 (> 99.9%) missing values Missing
nameAccordingTo has 4515660 (> 99.9%) missing values Missing
namePublishedInYear has 4515656 (> 99.9%) missing values Missing
phylum has 3795307 (84.0%) missing values Missing
class has 166450 (3.7%) missing values Missing
order has 53019 (1.2%) missing values Missing
family has 49040 (1.1%) missing values Missing
subgenus has 4515572 (> 99.9%) missing values Missing
infraspecificEpithet has 4196068 (92.9%) missing values Missing
cultivarEpithet has 4515659 (> 99.9%) missing values Missing
taxonRank has 4196350 (92.9%) missing values Missing
scientificNameAuthorship has 491289 (10.9%) missing values Missing
vernacularName has 4515658 (> 99.9%) missing values Missing
nomenclaturalCode has 4515660 (> 99.9%) missing values Missing
nomenclaturalStatus has 4515659 (> 99.9%) missing values Missing
taxonRemarks has 4515659 (> 99.9%) missing values Missing
gbifID has unique values Unique
occurrenceID has unique values Unique

Reproduction

Analysis started2025-03-04 19:33:49.973343
Analysis finished2025-03-04 19:37:22.407576
Duration3 minutes and 32.43 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct4515661
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:24.985034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters45156610
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

Unique4515661 ?
Unique (%)100.0%

Sample

1st row1320179379
2nd row1675994101
3rd row2592240144
4th row2571494932
5th row3357270605
ValueCountFrequency (%)
1320179379 1
 
< 0.1%
1320180447 1
 
< 0.1%
3897771070 1
 
< 0.1%
1320181031 1
 
< 0.1%
1321730416 1
 
< 0.1%
1321730340 1
 
< 0.1%
3467345455 1
 
< 0.1%
1456364699 1
 
< 0.1%
1321730091 1
 
< 0.1%
1320184062 1
 
< 0.1%
Other values (4515651) 4515651
> 99.9%
2025-03-04T14:37:27.465313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6587745
14.6%
2 6301614
14.0%
3 5901629
13.1%
5 4291608
9.5%
6 3896698
8.6%
4 3891530
8.6%
7 3755094
8.3%
8 3664337
8.1%
0 3571035
7.9%
9 3295320
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45156610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6587745
14.6%
2 6301614
14.0%
3 5901629
13.1%
5 4291608
9.5%
6 3896698
8.6%
4 3891530
8.6%
7 3755094
8.3%
8 3664337
8.1%
0 3571035
7.9%
9 3295320
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45156610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6587745
14.6%
2 6301614
14.0%
3 5901629
13.1%
5 4291608
9.5%
6 3896698
8.6%
4 3891530
8.6%
7 3755094
8.3%
8 3664337
8.1%
0 3571035
7.9%
9 3295320
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45156610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6587745
14.6%
2 6301614
14.0%
3 5901629
13.1%
5 4291608
9.5%
6 3896698
8.6%
4 3891530
8.6%
7 3755094
8.3%
8 3664337
8.1%
0 3571035
7.9%
9 3295320
7.3%
Distinct180669
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:27.611539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters85797559
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

Unique59498 ?
Unique (%)1.3%

Sample

1st row2016-08-30 13:42:00
2nd row2022-10-26 17:57:00
3rd row2020-05-10 23:06:00
4th row2020-04-09 11:53:00
5th row2021-09-10 21:16:00
ValueCountFrequency (%)
2017-08-04 233209
 
2.6%
2022-10-26 209132
 
2.3%
2022-06-03 121741
 
1.3%
2022-09-08 97141
 
1.1%
2017-12-19 94237
 
1.0%
2022-06-02 84448
 
0.9%
2024-10-17 76731
 
0.8%
2016-08-29 71251
 
0.8%
2016-08-30 70049
 
0.8%
2019-07-12 61211
 
0.7%
Other values (3909) 7912172
87.6%
2025-03-04T14:37:27.807650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23062874
26.9%
2 11939180
13.9%
1 11301135
13.2%
- 9031322
 
10.5%
: 9031322
 
10.5%
4515661
 
5.3%
3 2960518
 
3.5%
8 2607270
 
3.0%
9 2572066
 
3.0%
4 2388421
 
2.8%
Other values (3) 6387790
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85797559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23062874
26.9%
2 11939180
13.9%
1 11301135
13.2%
- 9031322
 
10.5%
: 9031322
 
10.5%
4515661
 
5.3%
3 2960518
 
3.5%
8 2607270
 
3.0%
9 2572066
 
3.0%
4 2388421
 
2.8%
Other values (3) 6387790
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85797559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23062874
26.9%
2 11939180
13.9%
1 11301135
13.2%
- 9031322
 
10.5%
: 9031322
 
10.5%
4515661
 
5.3%
3 2960518
 
3.5%
8 2607270
 
3.0%
9 2572066
 
3.0%
4 2388421
 
2.8%
Other values (3) 6387790
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85797559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23062874
26.9%
2 11939180
13.9%
1 11301135
13.2%
- 9031322
 
10.5%
: 9031322
 
10.5%
4515661
 
5.3%
3 2960518
 
3.5%
8 2607270
 
3.0%
9 2572066
 
3.0%
4 2388421
 
2.8%
Other values (3) 6387790
 
7.4%

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:27.856350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters130954169
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:15463
2nd rowurn:lsid:biocol.org:col:15463
3rd rowurn:lsid:biocol.org:col:15463
4th rowurn:lsid:biocol.org:col:15463
5th rowurn:lsid:biocol.org:col:15463
ValueCountFrequency (%)
urn:lsid:biocol.org:col:15463 4515661
100.0%
2025-03-04T14:37:27.935817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 18062644
13.8%
: 18062644
13.8%
l 13546983
 
10.3%
i 9031322
 
6.9%
r 9031322
 
6.9%
c 9031322
 
6.9%
g 4515661
 
3.4%
6 4515661
 
3.4%
4 4515661
 
3.4%
5 4515661
 
3.4%
Other values (8) 36125288
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130954169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 18062644
13.8%
: 18062644
13.8%
l 13546983
 
10.3%
i 9031322
 
6.9%
r 9031322
 
6.9%
c 9031322
 
6.9%
g 4515661
 
3.4%
6 4515661
 
3.4%
4 4515661
 
3.4%
5 4515661
 
3.4%
Other values (8) 36125288
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130954169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 18062644
13.8%
: 18062644
13.8%
l 13546983
 
10.3%
i 9031322
 
6.9%
r 9031322
 
6.9%
c 9031322
 
6.9%
g 4515661
 
3.4%
6 4515661
 
3.4%
4 4515661
 
3.4%
5 4515661
 
3.4%
Other values (8) 36125288
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130954169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 18062644
13.8%
: 18062644
13.8%
l 13546983
 
10.3%
i 9031322
 
6.9%
r 9031322
 
6.9%
c 9031322
 
6.9%
g 4515661
 
3.4%
6 4515661
 
3.4%
4 4515661
 
3.4%
5 4515661
 
3.4%
Other values (8) 36125288
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:27.965482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters203204745
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:60e28f81-e634-4869-aa3e-732caed713c8
2nd rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
3rd rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
4th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
5th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
ValueCountFrequency (%)
urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8 4515661
100.0%
2025-03-04T14:37:28.044970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 18062644
 
8.9%
3 18062644
 
8.9%
- 18062644
 
8.9%
e 18062644
 
8.9%
6 13546983
 
6.7%
a 13546983
 
6.7%
u 13546983
 
6.7%
d 9031322
 
4.4%
2 9031322
 
4.4%
1 9031322
 
4.4%
Other values (10) 63219254
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203204745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 18062644
 
8.9%
3 18062644
 
8.9%
- 18062644
 
8.9%
e 18062644
 
8.9%
6 13546983
 
6.7%
a 13546983
 
6.7%
u 13546983
 
6.7%
d 9031322
 
4.4%
2 9031322
 
4.4%
1 9031322
 
4.4%
Other values (10) 63219254
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203204745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 18062644
 
8.9%
3 18062644
 
8.9%
- 18062644
 
8.9%
e 18062644
 
8.9%
6 13546983
 
6.7%
a 13546983
 
6.7%
u 13546983
 
6.7%
d 9031322
 
4.4%
2 9031322
 
4.4%
1 9031322
 
4.4%
Other values (10) 63219254
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203204745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 18062644
 
8.9%
3 18062644
 
8.9%
- 18062644
 
8.9%
e 18062644
 
8.9%
6 13546983
 
6.7%
a 13546983
 
6.7%
u 13546983
 
6.7%
d 9031322
 
4.4%
2 9031322
 
4.4%
1 9031322
 
4.4%
Other values (10) 63219254
31.1%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:28.072114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9031322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 4515661
100.0%
2025-03-04T14:37:28.150495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9031322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9031322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9031322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:28.178502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9031322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 4515661
100.0%
2025-03-04T14:37:28.255528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9031322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9031322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9031322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4515661
50.0%
S 4515661
50.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:28.283596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters85797559
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 rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 4515661
33.3%
extant 4515661
33.3%
biology 4515661
33.3%
2025-03-04T14:37:28.366467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 9031322
 
10.5%
9031322
 
10.5%
t 9031322
 
10.5%
o 9031322
 
10.5%
M 4515661
 
5.3%
H 4515661
 
5.3%
E 4515661
 
5.3%
x 4515661
 
5.3%
a 4515661
 
5.3%
n 4515661
 
5.3%
Other values (5) 22578305
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85797559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 9031322
 
10.5%
9031322
 
10.5%
t 9031322
 
10.5%
o 9031322
 
10.5%
M 4515661
 
5.3%
H 4515661
 
5.3%
E 4515661
 
5.3%
x 4515661
 
5.3%
a 4515661
 
5.3%
n 4515661
 
5.3%
Other values (5) 22578305
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85797559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 9031322
 
10.5%
9031322
 
10.5%
t 9031322
 
10.5%
o 9031322
 
10.5%
M 4515661
 
5.3%
H 4515661
 
5.3%
E 4515661
 
5.3%
x 4515661
 
5.3%
a 4515661
 
5.3%
n 4515661
 
5.3%
Other values (5) 22578305
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85797559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 9031322
 
10.5%
9031322
 
10.5%
t 9031322
 
10.5%
o 9031322
 
10.5%
M 4515661
 
5.3%
H 4515661
 
5.3%
E 4515661
 
5.3%
x 4515661
 
5.3%
a 4515661
 
5.3%
n 4515661
 
5.3%
Other values (5) 22578305
26.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:28.396765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17.01103094
Min length16

Characters and Unicode

Total characters76816049
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPreservedSpecimen
2nd rowPreservedSpecimen
3rd rowPreservedSpecimen
4th rowPreservedSpecimen
5th rowPreservedSpecimen
ValueCountFrequency (%)
preservedspecimen 4465843
98.9%
machineobservation 49815
 
1.1%
humanobservation 3
 
< 0.1%
2025-03-04T14:37:28.493232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22428848
29.2%
r 8981504
11.7%
n 4565479
 
5.9%
i 4565476
 
5.9%
s 4515661
 
5.9%
v 4515661
 
5.9%
c 4515658
 
5.9%
m 4465846
 
5.8%
P 4465843
 
5.8%
p 4465843
 
5.8%
Other values (11) 9330230
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76816049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22428848
29.2%
r 8981504
11.7%
n 4565479
 
5.9%
i 4565476
 
5.9%
s 4515661
 
5.9%
v 4515661
 
5.9%
c 4515658
 
5.9%
m 4465846
 
5.8%
P 4465843
 
5.8%
p 4465843
 
5.8%
Other values (11) 9330230
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76816049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22428848
29.2%
r 8981504
11.7%
n 4565479
 
5.9%
i 4565476
 
5.9%
s 4515661
 
5.9%
v 4515661
 
5.9%
c 4515658
 
5.9%
m 4465846
 
5.8%
P 4465843
 
5.8%
p 4465843
 
5.8%
Other values (11) 9330230
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76816049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22428848
29.2%
r 8981504
11.7%
n 4565479
 
5.9%
i 4565476
 
5.9%
s 4515661
 
5.9%
v 4515661
 
5.9%
c 4515658
 
5.9%
m 4465846
 
5.8%
P 4465843
 
5.8%
p 4465843
 
5.8%
Other values (11) 9330230
12.1%

occurrenceID
Text

Unique 

Distinct4515661
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-04T14:37:30.685569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters284486643
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

Unique4515661 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/383aab1ce-8b35-4007-8eba-472b592b7a99
2nd rowhttp://n2t.net/ark:/65665/3c8351e79-8b3b-4df0-80be-cb019ba60185
3rd rowhttp://n2t.net/ark:/65665/3c8377593-a51b-4b6a-835d-649053b2ef0f
4th rowhttp://n2t.net/ark:/65665/383b388e9-b7cc-4b41-95cc-e0a1b092179a
5th rowhttp://n2t.net/ark:/65665/3c83e5abc-b64e-45a4-aa42-faf5abc93792
ValueCountFrequency (%)
http://n2t.net/ark:/65665/383aab1ce-8b35-4007-8eba-472b592b7a99 1
 
< 0.1%
http://n2t.net/ark:/65665/383b6d73e-eb70-4b52-81b8-336878ca92f0 1
 
< 0.1%
http://n2t.net/ark:/65665/3c84a8b17-83ab-45b2-bb8e-ccea78a7e003 1
 
< 0.1%
http://n2t.net/ark:/65665/383be1f82-08fe-4004-9374-3793b1df97c5 1
 
< 0.1%
http://n2t.net/ark:/65665/3c842b3ee-b36e-41da-867e-a7c09def7524 1
 
< 0.1%
http://n2t.net/ark:/65665/3c841ed6b-df48-4633-aae7-d3e846a86aa3 1
 
< 0.1%
http://n2t.net/ark:/65665/383b77fe7-0ea2-407e-bde8-bba5ef603c4a 1
 
< 0.1%
http://n2t.net/ark:/65665/3c8411b46-27ee-4d70-ab07-e1bd72f2e83a 1
 
< 0.1%
http://n2t.net/ark:/65665/3c83f60ef-2f0d-451e-986a-e0c2dfb03675 1
 
< 0.1%
http://n2t.net/ark:/65665/383e13640-df35-46e1-befc-1068e49e2444 1
 
< 0.1%
Other values (4515651) 4515651
> 99.9%
2025-03-04T14:37:33.040040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 22578305
 
7.9%
6 22016321
 
7.7%
- 18062644
 
6.3%
t 18062644
 
6.3%
5 17496925
 
6.2%
a 14109308
 
5.0%
e 12988442
 
4.6%
4 12983714
 
4.6%
2 12981604
 
4.6%
3 12976062
 
4.6%
Other values (16) 120230674
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284486643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 22578305
 
7.9%
6 22016321
 
7.7%
- 18062644
 
6.3%
t 18062644
 
6.3%
5 17496925
 
6.2%
a 14109308
 
5.0%
e 12988442
 
4.6%
4 12983714
 
4.6%
2 12981604
 
4.6%
3 12976062
 
4.6%
Other values (16) 120230674
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284486643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 22578305
 
7.9%
6 22016321
 
7.7%
- 18062644
 
6.3%
t 18062644
 
6.3%
5 17496925
 
6.2%
a 14109308
 
5.0%
e 12988442
 
4.6%
4 12983714
 
4.6%
2 12981604
 
4.6%
3 12976062
 
4.6%
Other values (16) 120230674
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284486643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 22578305
 
7.9%
6 22016321
 
7.7%
- 18062644
 
6.3%
t 18062644
 
6.3%
5 17496925
 
6.2%
a 14109308
 
5.0%
e 12988442
 
4.6%
4 12983714
 
4.6%
2 12981604
 
4.6%
3 12976062
 
4.6%
Other values (16) 120230674
42.3%

catalogNumber
Text

Missing 

Distinct3682462
Distinct (%)94.2%
Missing604654
Missing (%)13.4%
Memory size34.5 MiB
2025-03-04T14:37:35.023981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length10
Mean length9.635907069
Min length4

Characters and Unicode

Total characters37686100
Distinct characters48
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

Unique3481841 ?
Unique (%)89.0%

Sample

1st rowUS 213621
2nd rowUS 2144946
3rd rowUS 3113222
4th rowUS 2583825
5th rowUS 3026466
ValueCountFrequency (%)
us 3868231
49.7%
sem 238
 
< 0.1%
146
 
< 0.1%
stub 135
 
< 0.1%
1 133
 
< 0.1%
micrograph 103
 
< 0.1%
169920 59
 
< 0.1%
2 44
 
< 0.1%
3 40
 
< 0.1%
95340 36
 
< 0.1%
Other values (3682069) 3910894
50.3%
2025-03-04T14:37:36.954396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3911382
10.4%
U 3911009
10.4%
3869052
10.3%
2 3434134
9.1%
1 3365755
8.9%
3 3065165
8.1%
5 2346706
 
6.2%
6 2337766
 
6.2%
4 2336936
 
6.2%
7 2291395
 
6.1%
Other values (38) 6816800
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37686100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3911382
10.4%
U 3911009
10.4%
3869052
10.3%
2 3434134
9.1%
1 3365755
8.9%
3 3065165
8.1%
5 2346706
 
6.2%
6 2337766
 
6.2%
4 2336936
 
6.2%
7 2291395
 
6.1%
Other values (38) 6816800
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37686100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3911382
10.4%
U 3911009
10.4%
3869052
10.3%
2 3434134
9.1%
1 3365755
8.9%
3 3065165
8.1%
5 2346706
 
6.2%
6 2337766
 
6.2%
4 2336936
 
6.2%
7 2291395
 
6.1%
Other values (38) 6816800
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37686100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3911382
10.4%
U 3911009
10.4%
3869052
10.3%
2 3434134
9.1%
1 3365755
8.9%
3 3065165
8.1%
5 2346706
 
6.2%
6 2337766
 
6.2%
4 2336936
 
6.2%
7 2291395
 
6.1%
Other values (38) 6816800
18.1%
Distinct483515
Distinct (%)10.8%
Missing39547
Missing (%)0.9%
Memory size34.5 MiB
2025-03-04T14:37:37.187506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length100
Median length93
Mean length4.492128887
Min length1

Characters and Unicode

Total characters20107281
Distinct characters126
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

Unique349259 ?
Unique (%)7.8%

Sample

1st rowBLM-210-IV-11-B-TDS
2nd row4319
3rd row2429
4th row95426
5th row1414/512
ValueCountFrequency (%)
s.n 643632
 
13.5%
bureau 20598
 
0.4%
eyd 15904
 
0.3%
s 14313
 
0.3%
of 13865
 
0.3%
n 13794
 
0.3%
science 13409
 
0.3%
d&ml 12897
 
0.3%
12506
 
0.3%
h 8672
 
0.2%
Other values (337587) 3987307
83.8%
2025-03-04T14:37:37.535533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2407969
12.0%
2 1865104
9.3%
3 1606272
 
8.0%
4 1507264
 
7.5%
0 1450162
 
7.2%
5 1449381
 
7.2%
6 1399430
 
7.0%
. 1359512
 
6.8%
7 1317204
 
6.6%
8 1264749
 
6.3%
Other values (116) 4480234
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20107281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2407969
12.0%
2 1865104
9.3%
3 1606272
 
8.0%
4 1507264
 
7.5%
0 1450162
 
7.2%
5 1449381
 
7.2%
6 1399430
 
7.0%
. 1359512
 
6.8%
7 1317204
 
6.6%
8 1264749
 
6.3%
Other values (116) 4480234
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20107281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2407969
12.0%
2 1865104
9.3%
3 1606272
 
8.0%
4 1507264
 
7.5%
0 1450162
 
7.2%
5 1449381
 
7.2%
6 1399430
 
7.0%
. 1359512
 
6.8%
7 1317204
 
6.6%
8 1264749
 
6.3%
Other values (116) 4480234
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20107281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2407969
12.0%
2 1865104
9.3%
3 1606272
 
8.0%
4 1507264
 
7.5%
0 1450162
 
7.2%
5 1449381
 
7.2%
6 1399430
 
7.0%
. 1359512
 
6.8%
7 1317204
 
6.6%
8 1264749
 
6.3%
Other values (116) 4480234
22.3%

recordedBy
Text

Missing 

Distinct148160
Distinct (%)3.3%
Missing54378
Missing (%)1.2%
Memory size34.5 MiB
2025-03-04T14:37:37.693015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length207
Median length182
Mean length17.24845678
Min length1

Characters and Unicode

Total characters76950247
Distinct characters169
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

Unique68031 ?
Unique (%)1.5%

Sample

1st rowContinental Shelf Associates for the MMS/BLM
2nd rowJ. Soukup
3rd rowI. Morel
4th rowJ. Steyermark & Cora Steyermark
5th rowA. Oakes & -. Ellis
ValueCountFrequency (%)
1250739
 
7.3%
j 893043
 
5.2%
a 765542
 
4.5%
r 679021
 
4.0%
e 678845
 
4.0%
c 633969
 
3.7%
m 612375
 
3.6%
h 550473
 
3.2%
l 447598
 
2.6%
w 441682
 
2.6%
Other values (47910) 10064443
59.1%
2025-03-04T14:37:37.919879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12556447
16.3%
. 9149950
 
11.9%
e 4946918
 
6.4%
r 3620522
 
4.7%
a 3597020
 
4.7%
o 3035449
 
3.9%
n 3020373
 
3.9%
l 2882126
 
3.7%
i 2489174
 
3.2%
t 2009722
 
2.6%
Other values (159) 29642546
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76950247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12556447
16.3%
. 9149950
 
11.9%
e 4946918
 
6.4%
r 3620522
 
4.7%
a 3597020
 
4.7%
o 3035449
 
3.9%
n 3020373
 
3.9%
l 2882126
 
3.7%
i 2489174
 
3.2%
t 2009722
 
2.6%
Other values (159) 29642546
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76950247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12556447
16.3%
. 9149950
 
11.9%
e 4946918
 
6.4%
r 3620522
 
4.7%
a 3597020
 
4.7%
o 3035449
 
3.9%
n 3020373
 
3.9%
l 2882126
 
3.7%
i 2489174
 
3.2%
t 2009722
 
2.6%
Other values (159) 29642546
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76950247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12556447
16.3%
. 9149950
 
11.9%
e 4946918
 
6.4%
r 3620522
 
4.7%
a 3597020
 
4.7%
o 3035449
 
3.9%
n 3020373
 
3.9%
l 2882126
 
3.7%
i 2489174
 
3.2%
t 2009722
 
2.6%
Other values (159) 29642546
38.5%
Distinct22
Distinct (%)< 0.1%
Missing560
Missing (%)< 0.1%
Memory size34.5 MiB
2025-03-04T14:37:37.954598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.000007973
Min length1

Characters and Unicode

Total characters4515137
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1 4513894
> 99.9%
2 489
 
< 0.1%
0 306
 
< 0.1%
3 137
 
< 0.1%
4 94
 
< 0.1%
5 55
 
< 0.1%
6 40
 
< 0.1%
7 21
 
< 0.1%
8 16
 
< 0.1%
9 13
 
< 0.1%
Other values (12) 36
 
< 0.1%
2025-03-04T14:37:38.040036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4513935
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4515137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4513935
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4515137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4513935
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4515137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4513935
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

lifeStage
Text

Missing 

Distinct117
Distinct (%)< 0.1%
Missing4152582
Missing (%)92.0%
Memory size34.5 MiB
2025-03-04T14:37:38.071237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length9
Mean length10.22810187
Min length1

Characters and Unicode

Total characters3713609
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

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowFruiting
2nd rowIn bud
3rd rowFlowering
4th rowFlowering
5th rowImmature fruit
ValueCountFrequency (%)
flowering 233588
50.4%
fruiting 101100
21.8%
and 42692
 
9.2%
vegetative 23865
 
5.1%
fertile 18492
 
4.0%
in 8662
 
1.9%
bud 8099
 
1.7%
flower 7510
 
1.6%
fruit 7391
 
1.6%
sterile 3316
 
0.7%
Other values (52) 9209
 
2.0%
2025-03-04T14:37:38.171866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 489124
13.2%
n 386365
10.4%
r 377645
10.2%
e 366016
9.9%
g 358832
9.7%
F 358692
9.7%
l 267441
7.2%
o 243782
6.6%
w 243567
6.6%
t 181837
 
4.9%
Other values (34) 440308
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3713609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 489124
13.2%
n 386365
10.4%
r 377645
10.2%
e 366016
9.9%
g 358832
9.7%
F 358692
9.7%
l 267441
7.2%
o 243782
6.6%
w 243567
6.6%
t 181837
 
4.9%
Other values (34) 440308
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3713609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 489124
13.2%
n 386365
10.4%
r 377645
10.2%
e 366016
9.9%
g 358832
9.7%
F 358692
9.7%
l 267441
7.2%
o 243782
6.6%
w 243567
6.6%
t 181837
 
4.9%
Other values (34) 440308
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3713609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 489124
13.2%
n 386365
10.4%
r 377645
10.2%
e 366016
9.9%
g 358832
9.7%
F 358692
9.7%
l 267441
7.2%
o 243782
6.6%
w 243567
6.6%
t 181837
 
4.9%
Other values (34) 440308
11.9%

preparations
Text

Missing 

Distinct117
Distinct (%)0.1%
Missing4381212
Missing (%)97.0%
Memory size34.5 MiB
2025-03-04T14:37:38.288551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length154
Median length142
Mean length13.21074906
Min length3

Characters and Unicode

Total characters1776172
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

Unique35 ?
Unique (%)< 0.1%

Sample

1st rowWood Sample
2nd rowPhotograph
3rd rowMicroslide
4th rowPhotograph
5th rowPhotograph; Photograph
ValueCountFrequency (%)
sample 42484
18.8%
wood 42481
18.8%
microslide 41833
18.5%
photograph 33523
14.8%
individual 18796
8.3%
strewn 10233
 
4.5%
sem 6926
 
3.1%
micrograph 6518
 
2.9%
ink 5466
 
2.4%
and 3022
 
1.3%
Other values (80) 15061
 
6.7%
2025-03-04T14:37:38.412086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 203882
 
11.5%
i 158041
 
8.9%
d 125573
 
7.1%
l 110697
 
6.2%
a 110082
 
6.2%
r 106206
 
6.0%
e 101621
 
5.7%
91894
 
5.2%
p 85868
 
4.8%
h 73865
 
4.2%
Other values (45) 608443
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1776172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 203882
 
11.5%
i 158041
 
8.9%
d 125573
 
7.1%
l 110697
 
6.2%
a 110082
 
6.2%
r 106206
 
6.0%
e 101621
 
5.7%
91894
 
5.2%
p 85868
 
4.8%
h 73865
 
4.2%
Other values (45) 608443
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1776172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 203882
 
11.5%
i 158041
 
8.9%
d 125573
 
7.1%
l 110697
 
6.2%
a 110082
 
6.2%
r 106206
 
6.0%
e 101621
 
5.7%
91894
 
5.2%
p 85868
 
4.8%
h 73865
 
4.2%
Other values (45) 608443
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1776172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 203882
 
11.5%
i 158041
 
8.9%
d 125573
 
7.1%
l 110697
 
6.2%
a 110082
 
6.2%
r 106206
 
6.0%
e 101621
 
5.7%
91894
 
5.2%
p 85868
 
4.8%
h 73865
 
4.2%
Other values (45) 608443
34.3%

associatedMedia
Text

Missing 

Distinct4172762
Distinct (%)99.4%
Missing318147
Missing (%)7.0%
Memory size34.5 MiB
2025-03-04T14:37:40.978195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1040
Median length49
Mean length49.74545934
Min length48

Characters and Unicode

Total characters208807262
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

Unique4151953 ?
Unique (%)98.9%

Sample

1st rowhttps://collections.nmnh.si.edu/media/?i=12410529
2nd rowhttps://collections.nmnh.si.edu/media/?i=14440219
3rd rowhttps://collections.nmnh.si.edu/media/?i=14306337
4th rowhttps://collections.nmnh.si.edu/media/?i=15522674
5th rowhttps://collections.nmnh.si.edu/media/?i=15293772
ValueCountFrequency (%)
16574494 50
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=13965384 42
 
< 0.1%
16580564 35
 
< 0.1%
16582219 30
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=15413125 25
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16645032 22
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=15413478 21
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=10422983 19
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=15921416 17
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16645082 16
 
< 0.1%
Other values (4474099) 4510811
> 99.9%
2025-03-04T14:37:43.785002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 16790056
 
8.0%
i 16790056
 
8.0%
s 12592542
 
6.0%
e 12592542
 
6.0%
n 12592542
 
6.0%
. 12592542
 
6.0%
t 12592542
 
6.0%
h 8395028
 
4.0%
c 8395028
 
4.0%
o 8395028
 
4.0%
Other values (21) 87079356
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208807262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 16790056
 
8.0%
i 16790056
 
8.0%
s 12592542
 
6.0%
e 12592542
 
6.0%
n 12592542
 
6.0%
. 12592542
 
6.0%
t 12592542
 
6.0%
h 8395028
 
4.0%
c 8395028
 
4.0%
o 8395028
 
4.0%
Other values (21) 87079356
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208807262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 16790056
 
8.0%
i 16790056
 
8.0%
s 12592542
 
6.0%
e 12592542
 
6.0%
n 12592542
 
6.0%
. 12592542
 
6.0%
t 12592542
 
6.0%
h 8395028
 
4.0%
c 8395028
 
4.0%
o 8395028
 
4.0%
Other values (21) 87079356
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208807262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 16790056
 
8.0%
i 16790056
 
8.0%
s 12592542
 
6.0%
e 12592542
 
6.0%
n 12592542
 
6.0%
. 12592542
 
6.0%
t 12592542
 
6.0%
h 8395028
 
4.0%
c 8395028
 
4.0%
o 8395028
 
4.0%
Other values (21) 87079356
41.7%

associatedSequences
Text

Missing 

Distinct334
Distinct (%)95.2%
Missing4515310
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:43.829678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length499
Median length249
Mean length140.8803419
Min length49

Characters and Unicode

Total characters49449
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

Unique317 ?
Unique (%)90.3%

Sample

1st rowhttps://www.ncbi.nlm.nih.gov/gquery?term=ON553270
2nd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553291
3rd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553246
4th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553283
5th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=EU527211|https://www.ncbi.nlm.nih.gov/gquery?term=EU527308|https://www.ncbi.nlm.nih.gov/gquery?term=EU527261
ValueCountFrequency (%)
https://www.ncbi.nlm.nih.gov/gquery?term=jn837179|https://www.ncbi.nlm.nih.gov/gquery?term=jn837463|https://www.ncbi.nlm.nih.gov/gquery?term=jn837359|https://www.ncbi.nlm.nih.gov/gquery?term=jn837269 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837192|https://www.ncbi.nlm.nih.gov/gquery?term=jn837282|https://www.ncbi.nlm.nih.gov/gquery?term=jn837372|https://www.ncbi.nlm.nih.gov/gquery?term=jn837475 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=eu527212|https://www.ncbi.nlm.nih.gov/gquery?term=eu527309|https://www.ncbi.nlm.nih.gov/gquery?term=eu527262 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837150|https://www.ncbi.nlm.nih.gov/gquery?term=jn837436|https://www.ncbi.nlm.nih.gov/gquery?term=jn837330|https://www.ncbi.nlm.nih.gov/gquery?term=jn837240 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837187|https://www.ncbi.nlm.nih.gov/gquery?term=jn837470|https://www.ncbi.nlm.nih.gov/gquery?term=jn837367|https://www.ncbi.nlm.nih.gov/gquery?term=jn837277 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=kf989555|https://www.ncbi.nlm.nih.gov/gquery?term=kf989872|https://www.ncbi.nlm.nih.gov/gquery?term=kf989774|https://www.ncbi.nlm.nih.gov/gquery?term=kf989974|https://www.ncbi.nlm.nih.gov/gquery?term=kf989663 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837109|https://www.ncbi.nlm.nih.gov/gquery?term=jn837391|https://www.ncbi.nlm.nih.gov/gquery?term=jn837290|https://www.ncbi.nlm.nih.gov/gquery?term=jn837199 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=eu527241|https://www.ncbi.nlm.nih.gov/gquery?term=eu527291 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837183|https://www.ncbi.nlm.nih.gov/gquery?term=jn837467|https://www.ncbi.nlm.nih.gov/gquery?term=jn837363|https://www.ncbi.nlm.nih.gov/gquery?term=jn837273 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837191|https://www.ncbi.nlm.nih.gov/gquery?term=jn837281|https://www.ncbi.nlm.nih.gov/gquery?term=jn837371|https://www.ncbi.nlm.nih.gov/gquery?term=jn837474 2
 
0.6%
Other values (324) 331
94.3%
2025-03-04T14:37:43.916162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

occurrenceRemarks
Text

Missing 

Distinct28685
Distinct (%)31.3%
Missing4424129
Missing (%)98.0%
Memory size34.5 MiB
2025-03-04T14:37:44.062211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length54951
Median length2489
Mean length77.45450771
Min length1

Characters and Unicode

Total characters7089566
Distinct characters143
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

Unique24536 ?
Unique (%)26.8%

Sample

1st rowReceived as: seed
2nd rowTranscribed by digital volunteers
3rd rowBRG
4th rowTranscribed by digital volunteers; Original spelling as annotated and published is "subplebeia". Same (?) taxon re-published in Contr. U.S. Natl. Herb. 17: 46 (1913) with more explicit type citation. Unclear whether Lecidea subplebeia is a later homonym of Lecidea subplebeja Vain. (1890); Lecidea austrocalifornica Zahlbr. published as replacement name but citing Lecidea "subplebeja Nyl. apud Hasse". The latter name is superfluous if the original name is not a later homonym.
5th rowUS, NY
ValueCountFrequency (%)
by 38261
 
3.8%
transcribed 30348
 
3.0%
digital 30037
 
2.9%
volunteers 30020
 
2.9%
19663
 
1.9%
of 17327
 
1.7%
us 14702
 
1.4%
as 13939
 
1.4%
and 12959
 
1.3%
the 12017
 
1.2%
Other values (45812) 799986
78.5%
2025-03-04T14:37:44.292018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
923590
 
13.0%
e 568137
 
8.0%
a 443985
 
6.3%
i 409730
 
5.8%
t 348936
 
4.9%
n 338603
 
4.8%
o 337481
 
4.8%
r 330807
 
4.7%
l 294956
 
4.2%
s 270029
 
3.8%
Other values (133) 2823312
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7089566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
923590
 
13.0%
e 568137
 
8.0%
a 443985
 
6.3%
i 409730
 
5.8%
t 348936
 
4.9%
n 338603
 
4.8%
o 337481
 
4.8%
r 330807
 
4.7%
l 294956
 
4.2%
s 270029
 
3.8%
Other values (133) 2823312
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7089566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
923590
 
13.0%
e 568137
 
8.0%
a 443985
 
6.3%
i 409730
 
5.8%
t 348936
 
4.9%
n 338603
 
4.8%
o 337481
 
4.8%
r 330807
 
4.7%
l 294956
 
4.2%
s 270029
 
3.8%
Other values (133) 2823312
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7089566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
923590
 
13.0%
e 568137
 
8.0%
a 443985
 
6.3%
i 409730
 
5.8%
t 348936
 
4.9%
n 338603
 
4.8%
o 337481
 
4.8%
r 330807
 
4.7%
l 294956
 
4.2%
s 270029
 
3.8%
Other values (133) 2823312
39.8%

organismName
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:44.330199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.666666667
Min length5

Characters and Unicode

Total characters17
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row3018.0
2nd row300.0
3rd row1580.0
ValueCountFrequency (%)
3018.0 1
33.3%
300.0 1
33.3%
1580.0 1
33.3%
2025-03-04T14:37:44.412485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7
41.2%
. 3
17.6%
3 2
 
11.8%
1 2
 
11.8%
8 2
 
11.8%
5 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
41.2%
. 3
17.6%
3 2
 
11.8%
1 2
 
11.8%
8 2
 
11.8%
5 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
41.2%
. 3
17.6%
3 2
 
11.8%
1 2
 
11.8%
8 2
 
11.8%
5 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
41.2%
. 3
17.6%
3 2
 
11.8%
1 2
 
11.8%
8 2
 
11.8%
5 1
 
5.9%

eventType
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:44.442094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row-7.38
ValueCountFrequency (%)
7.38 1
100.0%
2025-03-04T14:37:44.520491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1
20.0%
7 1
20.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1
20.0%
7 1
20.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1
20.0%
7 1
20.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1
20.0%
7 1
20.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

fieldNumber
Text

Missing 

Distinct15
Distinct (%)5.7%
Missing4515399
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:44.552525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length9
Mean length9.122137405
Min length4

Characters and Unicode

Total characters2390
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

Unique8 ?
Unique (%)3.1%

Sample

1st rowSample OY
2nd rowSample OY
3rd rowSample OY
4th rowSample OY
5th rowSample OY
ValueCountFrequency (%)
sample 240
46.1%
oy 240
46.1%
koolau 5
 
1.0%
b 4
 
0.8%
a 4
 
0.8%
259 3
 
0.6%
l-52 3
 
0.6%
koolau_784 3
 
0.6%
17-v-88-5-n 2
 
0.4%
zeeland 2
 
0.4%
Other values (13) 15
 
2.9%
2025-03-04T14:37:44.644935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 263
11.0%
259
10.8%
l 255
10.7%
e 247
10.3%
S 241
10.1%
p 240
10.0%
m 240
10.0%
O 240
10.0%
Y 240
10.0%
o 19
 
0.8%
Other values (34) 146
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 263
11.0%
259
10.8%
l 255
10.7%
e 247
10.3%
S 241
10.1%
p 240
10.0%
m 240
10.0%
O 240
10.0%
Y 240
10.0%
o 19
 
0.8%
Other values (34) 146
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 263
11.0%
259
10.8%
l 255
10.7%
e 247
10.3%
S 241
10.1%
p 240
10.0%
m 240
10.0%
O 240
10.0%
Y 240
10.0%
o 19
 
0.8%
Other values (34) 146
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 263
11.0%
259
10.8%
l 255
10.7%
e 247
10.3%
S 241
10.1%
p 240
10.0%
m 240
10.0%
O 240
10.0%
Y 240
10.0%
o 19
 
0.8%
Other values (34) 146
6.1%

eventDate
Text

Missing 

Distinct100743
Distinct (%)2.5%
Missing499507
Missing (%)11.1%
Memory size34.5 MiB
2025-03-04T14:37:44.802827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length10
Mean length10.22911024
Min length4

Characters and Unicode

Total characters41081682
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

Unique26184 ?
Unique (%)0.7%

Sample

1st row1981-04-30
2nd row1954-08-07
3rd row1947-04-03
4th row1966-04-01
5th row1971-03-23
ValueCountFrequency (%)
or 7511
 
0.2%
1838/1842 6999
 
0.2%
1891 4648
 
0.1%
1760/1808 3657
 
0.1%
1889 3487
 
0.1%
1875 3339
 
0.1%
1853/1856 3324
 
0.1%
1890 3156
 
0.1%
1923 3139
 
0.1%
1887 3048
 
0.1%
Other values (96839) 3988868
99.0%
2025-03-04T14:37:45.043446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7801445
19.0%
- 7726696
18.8%
0 6238271
15.2%
9 5176217
12.6%
2 3077613
 
7.5%
8 2443585
 
5.9%
7 1744336
 
4.2%
6 1741318
 
4.2%
3 1670243
 
4.1%
5 1552877
 
3.8%
Other values (6) 1909081
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41081682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7801445
19.0%
- 7726696
18.8%
0 6238271
15.2%
9 5176217
12.6%
2 3077613
 
7.5%
8 2443585
 
5.9%
7 1744336
 
4.2%
6 1741318
 
4.2%
3 1670243
 
4.1%
5 1552877
 
3.8%
Other values (6) 1909081
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41081682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7801445
19.0%
- 7726696
18.8%
0 6238271
15.2%
9 5176217
12.6%
2 3077613
 
7.5%
8 2443585
 
5.9%
7 1744336
 
4.2%
6 1741318
 
4.2%
3 1670243
 
4.1%
5 1552877
 
3.8%
Other values (6) 1909081
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41081682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7801445
19.0%
- 7726696
18.8%
0 6238271
15.2%
9 5176217
12.6%
2 3077613
 
7.5%
8 2443585
 
5.9%
7 1744336
 
4.2%
6 1741318
 
4.2%
3 1670243
 
4.1%
5 1552877
 
3.8%
Other values (6) 1909081
 
4.6%

startDayOfYear
Text

Missing 

Distinct366
Distinct (%)< 0.1%
Missing707937
Missing (%)15.7%
Memory size34.5 MiB
2025-03-04T14:37:45.194109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.786185921
Min length1

Characters and Unicode

Total characters10609027
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 row120
2nd row219
3rd row93
4th row91
5th row82
ValueCountFrequency (%)
212 65252
 
1.7%
243 53926
 
1.4%
181 53248
 
1.4%
151 48909
 
1.3%
120 37888
 
1.0%
213 35186
 
0.9%
273 34755
 
0.9%
90 31604
 
0.8%
304 30708
 
0.8%
244 28680
 
0.8%
Other values (356) 3387568
89.0%
2025-03-04T14:37:45.402559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2141855
20.2%
2 2135625
20.1%
3 1271069
12.0%
4 814683
 
7.7%
5 784204
 
7.4%
0 747144
 
7.0%
9 692015
 
6.5%
6 686000
 
6.5%
8 680008
 
6.4%
7 656424
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10609027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2141855
20.2%
2 2135625
20.1%
3 1271069
12.0%
4 814683
 
7.7%
5 784204
 
7.4%
0 747144
 
7.0%
9 692015
 
6.5%
6 686000
 
6.5%
8 680008
 
6.4%
7 656424
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10609027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2141855
20.2%
2 2135625
20.1%
3 1271069
12.0%
4 814683
 
7.7%
5 784204
 
7.4%
0 747144
 
7.0%
9 692015
 
6.5%
6 686000
 
6.5%
8 680008
 
6.4%
7 656424
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10609027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2141855
20.2%
2 2135625
20.1%
3 1271069
12.0%
4 814683
 
7.7%
5 784204
 
7.4%
0 747144
 
7.0%
9 692015
 
6.5%
6 686000
 
6.5%
8 680008
 
6.4%
7 656424
 
6.2%

endDayOfYear
Text

Missing 

Distinct366
Distinct (%)< 0.1%
Missing706297
Missing (%)15.6%
Memory size34.5 MiB
2025-03-04T14:37:45.541018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.787330116
Min length1

Characters and Unicode

Total characters10617955
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 row120
2nd row219
3rd row93
4th row91
5th row82
ValueCountFrequency (%)
212 66123
 
1.7%
243 57269
 
1.5%
181 53207
 
1.4%
151 44291
 
1.2%
120 37736
 
1.0%
273 36145
 
0.9%
90 33356
 
0.9%
304 33021
 
0.9%
213 32570
 
0.9%
244 30634
 
0.8%
Other values (356) 3385012
88.9%
2025-03-04T14:37:45.743246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2140880
20.2%
1 2116002
19.9%
3 1288304
12.1%
4 826923
 
7.8%
5 780021
 
7.3%
0 749805
 
7.1%
9 689036
 
6.5%
6 683421
 
6.4%
8 681866
 
6.4%
7 661697
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10617955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2140880
20.2%
1 2116002
19.9%
3 1288304
12.1%
4 826923
 
7.8%
5 780021
 
7.3%
0 749805
 
7.1%
9 689036
 
6.5%
6 683421
 
6.4%
8 681866
 
6.4%
7 661697
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10617955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2140880
20.2%
1 2116002
19.9%
3 1288304
12.1%
4 826923
 
7.8%
5 780021
 
7.3%
0 749805
 
7.1%
9 689036
 
6.5%
6 683421
 
6.4%
8 681866
 
6.4%
7 661697
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10617955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2140880
20.2%
1 2116002
19.9%
3 1288304
12.1%
4 826923
 
7.8%
5 780021
 
7.3%
0 749805
 
7.1%
9 689036
 
6.5%
6 683421
 
6.4%
8 681866
 
6.4%
7 661697
 
6.2%

year
Text

Missing 

Distinct275
Distinct (%)< 0.1%
Missing499507
Missing (%)11.1%
Memory size34.5 MiB
2025-03-04T14:37:45.891124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16064616
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

Unique14 ?
Unique (%)< 0.1%

Sample

1st row1981
2nd row1954
3rd row1947
4th row1966
5th row1971
ValueCountFrequency (%)
1966 52747
 
1.3%
1964 51506
 
1.3%
1939 48539
 
1.2%
1949 46743
 
1.2%
1929 45873
 
1.1%
1938 44996
 
1.1%
1965 44897
 
1.1%
1922 42897
 
1.1%
1962 42322
 
1.1%
1968 41351
 
1.0%
Other values (265) 3554283
88.5%
2025-03-04T14:37:46.100876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4564194
28.4%
9 4113450
25.6%
8 1419370
 
8.8%
0 1047460
 
6.5%
2 964799
 
6.0%
6 886880
 
5.5%
4 787306
 
4.9%
3 776965
 
4.8%
7 755520
 
4.7%
5 748672
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16064616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4564194
28.4%
9 4113450
25.6%
8 1419370
 
8.8%
0 1047460
 
6.5%
2 964799
 
6.0%
6 886880
 
5.5%
4 787306
 
4.9%
3 776965
 
4.8%
7 755520
 
4.7%
5 748672
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16064616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4564194
28.4%
9 4113450
25.6%
8 1419370
 
8.8%
0 1047460
 
6.5%
2 964799
 
6.0%
6 886880
 
5.5%
4 787306
 
4.9%
3 776965
 
4.8%
7 755520
 
4.7%
5 748672
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16064616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4564194
28.4%
9 4113450
25.6%
8 1419370
 
8.8%
0 1047460
 
6.5%
2 964799
 
6.0%
6 886880
 
5.5%
4 787306
 
4.9%
3 776965
 
4.8%
7 755520
 
4.7%
5 748672
 
4.7%

month
Text

Missing 

Distinct12
Distinct (%)< 0.1%
Missing700051
Missing (%)15.5%
Memory size34.5 MiB
2025-03-04T14:37:46.154132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.171101868
Min length1

Characters and Unicode

Total characters4468468
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 row4
2nd row8
3rd row4
4th row4
5th row3
ValueCountFrequency (%)
7 547270
14.3%
8 491637
12.9%
6 411350
10.8%
5 354199
9.3%
9 340693
8.9%
4 293710
7.7%
3 268012
7.0%
10 260725
6.8%
2 235530
6.2%
1 220351
5.8%
Other values (2) 392133
10.3%
2025-03-04T14:37:46.241934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1080650
24.2%
7 547270
12.2%
8 491637
11.0%
2 420222
 
9.4%
6 411350
 
9.2%
5 354199
 
7.9%
9 340693
 
7.6%
4 293710
 
6.6%
3 268012
 
6.0%
0 260725
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4468468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1080650
24.2%
7 547270
12.2%
8 491637
11.0%
2 420222
 
9.4%
6 411350
 
9.2%
5 354199
 
7.9%
9 340693
 
7.6%
4 293710
 
6.6%
3 268012
 
6.0%
0 260725
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4468468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1080650
24.2%
7 547270
12.2%
8 491637
11.0%
2 420222
 
9.4%
6 411350
 
9.2%
5 354199
 
7.9%
9 340693
 
7.6%
4 293710
 
6.6%
3 268012
 
6.0%
0 260725
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4468468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1080650
24.2%
7 547270
12.2%
8 491637
11.0%
2 420222
 
9.4%
6 411350
 
9.2%
5 354199
 
7.9%
9 340693
 
7.6%
4 293710
 
6.6%
3 268012
 
6.0%
0 260725
 
5.8%

day
Text

Missing 

Distinct31
Distinct (%)< 0.1%
Missing1180026
Missing (%)26.1%
Memory size34.5 MiB
2025-03-04T14:37:46.286699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.709506586
Min length1

Characters and Unicode

Total characters5702290
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 row30
2nd row7
3rd row3
4th row1
5th row23
ValueCountFrequency (%)
20 124820
 
3.7%
15 121526
 
3.6%
10 116402
 
3.5%
1 116290
 
3.5%
18 114533
 
3.4%
25 112462
 
3.4%
19 112076
 
3.4%
17 111731
 
3.3%
12 111029
 
3.3%
8 109940
 
3.3%
Other values (21) 2184826
65.5%
2025-03-04T14:37:46.393152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1506435
26.4%
2 1419764
24.9%
3 471332
 
8.3%
5 342700
 
6.0%
0 340158
 
6.0%
8 333415
 
5.8%
7 328337
 
5.8%
6 325244
 
5.7%
4 321679
 
5.6%
9 313226
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5702290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1506435
26.4%
2 1419764
24.9%
3 471332
 
8.3%
5 342700
 
6.0%
0 340158
 
6.0%
8 333415
 
5.8%
7 328337
 
5.8%
6 325244
 
5.7%
4 321679
 
5.6%
9 313226
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5702290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1506435
26.4%
2 1419764
24.9%
3 471332
 
8.3%
5 342700
 
6.0%
0 340158
 
6.0%
8 333415
 
5.8%
7 328337
 
5.8%
6 325244
 
5.7%
4 321679
 
5.6%
9 313226
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5702290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1506435
26.4%
2 1419764
24.9%
3 471332
 
8.3%
5 342700
 
6.0%
0 340158
 
6.0%
8 333415
 
5.8%
7 328337
 
5.8%
6 325244
 
5.7%
4 321679
 
5.6%
9 313226
 
5.5%

verbatimEventDate
Text

Missing 

Distinct144603
Distinct (%)9.5%
Missing2995056
Missing (%)66.3%
Memory size34.5 MiB
2025-03-04T14:37:46.570163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length44726
Median length11
Mean length13.4161199
Min length1

Characters and Unicode

Total characters20400619
Distinct characters110
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

Unique47164 ?
Unique (%)3.1%

Sample

1st row30 Apr 1981
2nd row16 Dec 1953
3rd row-- --- ----
4th row01 Feb 1974
5th rowTranscribed d/m/y: 28/4/76
ValueCountFrequency (%)
569685
 
12.1%
transcribed 163800
 
3.5%
d/m/y 163799
 
3.5%
jul 133516
 
2.8%
aug 127314
 
2.7%
may 100704
 
2.1%
sep 100407
 
2.1%
jun 99914
 
2.1%
mar 89911
 
1.9%
to 89366
 
1.9%
Other values (50980) 3069137
65.2%
2025-03-04T14:37:46.870174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3184972
15.6%
1 2057978
 
10.1%
- 1706716
 
8.4%
9 1491498
 
7.3%
2 903531
 
4.4%
0 760159
 
3.7%
/ 668632
 
3.3%
8 665330
 
3.3%
r 587998
 
2.9%
e 493324
 
2.4%
Other values (100) 7880481
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20400619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3184972
15.6%
1 2057978
 
10.1%
- 1706716
 
8.4%
9 1491498
 
7.3%
2 903531
 
4.4%
0 760159
 
3.7%
/ 668632
 
3.3%
8 665330
 
3.3%
r 587998
 
2.9%
e 493324
 
2.4%
Other values (100) 7880481
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20400619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3184972
15.6%
1 2057978
 
10.1%
- 1706716
 
8.4%
9 1491498
 
7.3%
2 903531
 
4.4%
0 760159
 
3.7%
/ 668632
 
3.3%
8 665330
 
3.3%
r 587998
 
2.9%
e 493324
 
2.4%
Other values (100) 7880481
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20400619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3184972
15.6%
1 2057978
 
10.1%
- 1706716
 
8.4%
9 1491498
 
7.3%
2 903531
 
4.4%
0 760159
 
3.7%
/ 668632
 
3.3%
8 665330
 
3.3%
r 587998
 
2.9%
e 493324
 
2.4%
Other values (100) 7880481
38.6%

habitat
Text

Missing 

Distinct179316
Distinct (%)35.4%
Missing4009333
Missing (%)88.8%
Memory size34.5 MiB
2025-03-04T14:37:47.021549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length98062
Median length506
Mean length33.74884265
Min length1

Characters and Unicode

Total characters17087984
Distinct characters145
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

Unique136896 ?
Unique (%)27.0%

Sample

1st rowErect.
2nd rowPlanted
3rd rowHillsides covered with broad-leaved forest, understory with Arthrostylidium, Rubus, and numerous ferns, epiphytes and Usnea.
4th rowOpen to closed forest with Pinus contorta, Populus tremuloides, Purshia tridentata, and Ribes cereum.
5th rowDeep secondary forest; clay soil
ValueCountFrequency (%)
forest 131247
 
5.0%
on 90052
 
3.4%
and 73888
 
2.8%
in 67428
 
2.6%
with 54107
 
2.1%
of 49767
 
1.9%
along 29262
 
1.1%
de 27744
 
1.1%
soil 24644
 
0.9%
slopes 22332
 
0.9%
Other values (44622) 2044337
78.2%
2025-03-04T14:37:47.260339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2101244
 
12.3%
e 1541379
 
9.0%
a 1348535
 
7.9%
o 1227275
 
7.2%
r 1069847
 
6.3%
s 1066411
 
6.2%
n 1051888
 
6.2%
i 901575
 
5.3%
t 853198
 
5.0%
l 666293
 
3.9%
Other values (135) 5260339
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17087984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2101244
 
12.3%
e 1541379
 
9.0%
a 1348535
 
7.9%
o 1227275
 
7.2%
r 1069847
 
6.3%
s 1066411
 
6.2%
n 1051888
 
6.2%
i 901575
 
5.3%
t 853198
 
5.0%
l 666293
 
3.9%
Other values (135) 5260339
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17087984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2101244
 
12.3%
e 1541379
 
9.0%
a 1348535
 
7.9%
o 1227275
 
7.2%
r 1069847
 
6.3%
s 1066411
 
6.2%
n 1051888
 
6.2%
i 901575
 
5.3%
t 853198
 
5.0%
l 666293
 
3.9%
Other values (135) 5260339
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17087984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2101244
 
12.3%
e 1541379
 
9.0%
a 1348535
 
7.9%
o 1227275
 
7.2%
r 1069847
 
6.3%
s 1066411
 
6.2%
n 1051888
 
6.2%
i 901575
 
5.3%
t 853198
 
5.0%
l 666293
 
3.9%
Other values (135) 5260339
30.8%

samplingProtocol
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:47.304570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
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

Unique1 ?
Unique (%)100.0%

Sample

1st row400.0
ValueCountFrequency (%)
400.0 1
100.0%
2025-03-04T14:37:47.386556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3
60.0%
4 1
 
20.0%
. 1
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3
60.0%
4 1
 
20.0%
. 1
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3
60.0%
4 1
 
20.0%
. 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3
60.0%
4 1
 
20.0%
. 1
 
20.0%

sampleSizeValue
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing4515659
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:47.415384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
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

Unique2 ?
Unique (%)100.0%

Sample

1st row500.0
2nd row1000.0
ValueCountFrequency (%)
500.0 1
50.0%
1000.0 1
50.0%
2025-03-04T14:37:47.499677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7
63.6%
. 2
 
18.2%
5 1
 
9.1%
1 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
63.6%
. 2
 
18.2%
5 1
 
9.1%
1 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
63.6%
. 2
 
18.2%
5 1
 
9.1%
1 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
63.6%
. 2
 
18.2%
5 1
 
9.1%
1 1
 
9.1%

locationID
Text

Missing 

Distinct1108
Distinct (%)2.7%
Missing4473993
Missing (%)99.1%
Memory size34.5 MiB
2025-03-04T14:37:47.629233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length37
Median length5
Mean length5.995968129
Min length1

Characters and Unicode

Total characters249840
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

Unique362 ?
Unique (%)0.9%

Sample

1st row66-10
2nd row69-11
3rd row64-51
4th row66-14
5th row64-34
ValueCountFrequency (%)
station 4948
 
10.3%
ms04 1735
 
3.6%
66-24 1381
 
2.9%
61 946
 
2.0%
64-48 628
 
1.3%
64-47 588
 
1.2%
69-14 562
 
1.2%
66-39 462
 
1.0%
66-28 442
 
0.9%
66-17 426
 
0.9%
Other values (1007) 36062
74.8%
2025-03-04T14:37:47.838502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 43817
17.5%
- 36322
14.5%
4 21703
 
8.7%
2 20165
 
8.1%
1 18260
 
7.3%
0 15408
 
6.2%
3 11182
 
4.5%
7 10459
 
4.2%
t 10132
 
4.1%
8 7703
 
3.1%
Other values (62) 54689
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 43817
17.5%
- 36322
14.5%
4 21703
 
8.7%
2 20165
 
8.1%
1 18260
 
7.3%
0 15408
 
6.2%
3 11182
 
4.5%
7 10459
 
4.2%
t 10132
 
4.1%
8 7703
 
3.1%
Other values (62) 54689
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 43817
17.5%
- 36322
14.5%
4 21703
 
8.7%
2 20165
 
8.1%
1 18260
 
7.3%
0 15408
 
6.2%
3 11182
 
4.5%
7 10459
 
4.2%
t 10132
 
4.1%
8 7703
 
3.1%
Other values (62) 54689
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 43817
17.5%
- 36322
14.5%
4 21703
 
8.7%
2 20165
 
8.1%
1 18260
 
7.3%
0 15408
 
6.2%
3 11182
 
4.5%
7 10459
 
4.2%
t 10132
 
4.1%
8 7703
 
3.1%
Other values (62) 54689
21.9%
Distinct29227
Distinct (%)0.7%
Missing38628
Missing (%)0.9%
Memory size34.5 MiB
2025-03-04T14:37:47.991209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length118
Mean length40.94742545
Min length4

Characters and Unicode

Total characters183322975
Distinct characters163
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

Unique9052 ?
Unique (%)0.2%

Sample

1st rowNorth America, United States, Florida
2nd rowSouth America - Neotropics, Peru, Piura
3rd rowSouth America, Argentina, Formosa
4th rowSouth America - Neotropics, Venezuela, Carabobo
5th rowAfrica, South Africa
ValueCountFrequency (%)
america 3037857
 
12.5%
north 1751165
 
7.2%
1668629
 
6.8%
neotropics 1604866
 
6.6%
united 1351633
 
5.5%
states 1342475
 
5.5%
south 1161951
 
4.8%
mexico 330187
 
1.4%
asia-tropical 303950
 
1.2%
brazil 299921
 
1.2%
Other values (15123) 11516200
47.3%
2025-03-04T14:37:48.221266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19891801
 
10.9%
a 16902477
 
9.2%
i 13659346
 
7.5%
e 13427066
 
7.3%
r 11621631
 
6.3%
t 11453417
 
6.2%
o 11169195
 
6.1%
, 9361908
 
5.1%
n 7219729
 
3.9%
c 7113809
 
3.9%
Other values (153) 61502596
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183322975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19891801
 
10.9%
a 16902477
 
9.2%
i 13659346
 
7.5%
e 13427066
 
7.3%
r 11621631
 
6.3%
t 11453417
 
6.2%
o 11169195
 
6.1%
, 9361908
 
5.1%
n 7219729
 
3.9%
c 7113809
 
3.9%
Other values (153) 61502596
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183322975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19891801
 
10.9%
a 16902477
 
9.2%
i 13659346
 
7.5%
e 13427066
 
7.3%
r 11621631
 
6.3%
t 11453417
 
6.2%
o 11169195
 
6.1%
, 9361908
 
5.1%
n 7219729
 
3.9%
c 7113809
 
3.9%
Other values (153) 61502596
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183322975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19891801
 
10.9%
a 16902477
 
9.2%
i 13659346
 
7.5%
e 13427066
 
7.3%
r 11621631
 
6.3%
t 11453417
 
6.2%
o 11169195
 
6.1%
, 9361908
 
5.1%
n 7219729
 
3.9%
c 7113809
 
3.9%
Other values (153) 61502596
33.5%

continent
Text

Missing 

Distinct72
Distinct (%)< 0.1%
Missing66158
Missing (%)1.5%
Memory size34.5 MiB
2025-03-04T14:37:48.264055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length57
Median length50
Mean length17.22849923
Min length4

Characters and Unicode

Total characters76658259
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

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowNorth America
2nd rowSouth America - Neotropics
3rd rowSouth America
4th rowSouth America - Neotropics
5th rowAfrica
ValueCountFrequency (%)
america 3037856
27.2%
north 1705271
15.3%
1605982
14.4%
neotropics 1604866
14.4%
south 1078261
 
9.7%
asia-tropical 303950
 
2.7%
central 269843
 
2.4%
west 265195
 
2.4%
indies 265195
 
2.4%
europe 230947
 
2.1%
Other values (19) 796938
 
7.1%
2025-03-04T14:37:48.362884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 7607800
 
9.9%
6714801
 
8.8%
o 6532692
 
8.5%
e 6336991
 
8.3%
i 6293342
 
8.2%
c 5457494
 
7.1%
t 5250425
 
6.8%
a 5072337
 
6.6%
A 3811578
 
5.0%
N 3310135
 
4.3%
Other values (30) 20270664
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76658259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 7607800
 
9.9%
6714801
 
8.8%
o 6532692
 
8.5%
e 6336991
 
8.3%
i 6293342
 
8.2%
c 5457494
 
7.1%
t 5250425
 
6.8%
a 5072337
 
6.6%
A 3811578
 
5.0%
N 3310135
 
4.3%
Other values (30) 20270664
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76658259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 7607800
 
9.9%
6714801
 
8.8%
o 6532692
 
8.5%
e 6336991
 
8.3%
i 6293342
 
8.2%
c 5457494
 
7.1%
t 5250425
 
6.8%
a 5072337
 
6.6%
A 3811578
 
5.0%
N 3310135
 
4.3%
Other values (30) 20270664
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76658259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 7607800
 
9.9%
6714801
 
8.8%
o 6532692
 
8.5%
e 6336991
 
8.3%
i 6293342
 
8.2%
c 5457494
 
7.1%
t 5250425
 
6.8%
a 5072337
 
6.6%
A 3811578
 
5.0%
N 3310135
 
4.3%
Other values (30) 20270664
26.4%

waterBody
Text

Missing 

Distinct146
Distinct (%)0.7%
Missing4496088
Missing (%)99.6%
Memory size34.5 MiB
2025-03-04T14:37:48.400298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length62
Median length61
Mean length26.33765902
Min length4

Characters and Unicode

Total characters515507
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

Unique67 ?
Unique (%)0.3%

Sample

1st rowNorth Atlantic Ocean, Bay of Fundy
2nd rowNorth Atlantic Ocean, Caribbean Sea
3rd rowNorth Atlantic Ocean, Gulf of Maine, Englishman Bay/Mack Cove
4th rowNorth Atlantic Ocean, Caribbean Sea
5th rowNorth Pacific Ocean
ValueCountFrequency (%)
ocean 15739
19.7%
north 15146
19.0%
atlantic 14293
17.9%
sea 7188
9.0%
caribbean 6009
 
7.5%
of 3765
 
4.7%
gulf 3584
 
4.5%
maine 2788
 
3.5%
bay 2525
 
3.2%
pacific 1232
 
1.5%
Other values (153) 7583
9.5%
2025-03-04T14:37:48.509530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60279
11.7%
a 59587
11.6%
t 46425
 
9.0%
n 42137
 
8.2%
e 36547
 
7.1%
c 34220
 
6.6%
i 27759
 
5.4%
r 23435
 
4.5%
o 23395
 
4.5%
l 19298
 
3.7%
Other values (48) 142425
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 515507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60279
11.7%
a 59587
11.6%
t 46425
 
9.0%
n 42137
 
8.2%
e 36547
 
7.1%
c 34220
 
6.6%
i 27759
 
5.4%
r 23435
 
4.5%
o 23395
 
4.5%
l 19298
 
3.7%
Other values (48) 142425
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 515507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60279
11.7%
a 59587
11.6%
t 46425
 
9.0%
n 42137
 
8.2%
e 36547
 
7.1%
c 34220
 
6.6%
i 27759
 
5.4%
r 23435
 
4.5%
o 23395
 
4.5%
l 19298
 
3.7%
Other values (48) 142425
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 515507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60279
11.7%
a 59587
11.6%
t 46425
 
9.0%
n 42137
 
8.2%
e 36547
 
7.1%
c 34220
 
6.6%
i 27759
 
5.4%
r 23435
 
4.5%
o 23395
 
4.5%
l 19298
 
3.7%
Other values (48) 142425
27.6%

islandGroup
Text

Missing 

Distinct535
Distinct (%)0.5%
Missing4403077
Missing (%)97.5%
Memory size34.5 MiB
2025-03-04T14:37:48.645402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length41
Mean length14.86225396
Min length3

Characters and Unicode

Total characters1673252
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

Unique124 ?
Unique (%)0.1%

Sample

1st rowGreater Antilles
2nd rowGreater Antilles
3rd rowElizabeth Islands
4th rowChannel Islands
5th rowGreater Antilles
ValueCountFrequency (%)
antilles 32223
 
12.5%
greater 32220
 
12.5%
islands 23341
 
9.1%
is 19916
 
7.7%
group 16200
 
6.3%
new 7374
 
2.9%
guinea 6014
 
2.3%
channel 5394
 
2.1%
keys 5332
 
2.1%
florida 5071
 
2.0%
Other values (442) 104053
40.5%
2025-03-04T14:37:48.857063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 168595
 
10.1%
a 153468
 
9.2%
144554
 
8.6%
s 133119
 
8.0%
l 127504
 
7.6%
r 120065
 
7.2%
n 113308
 
6.8%
t 87870
 
5.3%
i 82774
 
4.9%
G 60256
 
3.6%
Other values (54) 481739
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1673252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 168595
 
10.1%
a 153468
 
9.2%
144554
 
8.6%
s 133119
 
8.0%
l 127504
 
7.6%
r 120065
 
7.2%
n 113308
 
6.8%
t 87870
 
5.3%
i 82774
 
4.9%
G 60256
 
3.6%
Other values (54) 481739
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1673252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 168595
 
10.1%
a 153468
 
9.2%
144554
 
8.6%
s 133119
 
8.0%
l 127504
 
7.6%
r 120065
 
7.2%
n 113308
 
6.8%
t 87870
 
5.3%
i 82774
 
4.9%
G 60256
 
3.6%
Other values (54) 481739
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1673252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 168595
 
10.1%
a 153468
 
9.2%
144554
 
8.6%
s 133119
 
8.0%
l 127504
 
7.6%
r 120065
 
7.2%
n 113308
 
6.8%
t 87870
 
5.3%
i 82774
 
4.9%
G 60256
 
3.6%
Other values (54) 481739
28.8%

island
Text

Missing 

Distinct4293
Distinct (%)1.1%
Missing4139168
Missing (%)91.7%
Memory size34.5 MiB
2025-03-04T14:37:48.999671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length43
Mean length9.545619706
Min length1

Characters and Unicode

Total characters3593859
Distinct characters86
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

Unique1311 ?
Unique (%)0.3%

Sample

1st rowRota
2nd rowHispaniola
3rd rowNorth Island
4th rowKaua'i
5th rowHispaniola Island
ValueCountFrequency (%)
hispaniola 49160
 
8.5%
island 45105
 
7.8%
cuba 23045
 
4.0%
oahu 17038
 
2.9%
st 12448
 
2.2%
kaua'i 12005
 
2.1%
new 10482
 
1.8%
isla 9883
 
1.7%
jamaica 9865
 
1.7%
luzon 9652
 
1.7%
Other values (3257) 379339
65.6%
2025-03-04T14:37:49.218439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 572883
15.9%
i 282904
 
7.9%
n 241731
 
6.7%
o 215280
 
6.0%
201529
 
5.6%
l 189222
 
5.3%
u 174379
 
4.9%
e 170799
 
4.8%
s 161533
 
4.5%
r 127280
 
3.5%
Other values (76) 1256319
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3593859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 572883
15.9%
i 282904
 
7.9%
n 241731
 
6.7%
o 215280
 
6.0%
201529
 
5.6%
l 189222
 
5.3%
u 174379
 
4.9%
e 170799
 
4.8%
s 161533
 
4.5%
r 127280
 
3.5%
Other values (76) 1256319
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3593859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 572883
15.9%
i 282904
 
7.9%
n 241731
 
6.7%
o 215280
 
6.0%
201529
 
5.6%
l 189222
 
5.3%
u 174379
 
4.9%
e 170799
 
4.8%
s 161533
 
4.5%
r 127280
 
3.5%
Other values (76) 1256319
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3593859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 572883
15.9%
i 282904
 
7.9%
n 241731
 
6.7%
o 215280
 
6.0%
201529
 
5.6%
l 189222
 
5.3%
u 174379
 
4.9%
e 170799
 
4.8%
s 161533
 
4.5%
r 127280
 
3.5%
Other values (76) 1256319
35.0%
Distinct460
Distinct (%)< 0.1%
Missing38684
Missing (%)0.9%
Memory size34.5 MiB
2025-03-04T14:37:49.370386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length50
Mean length9.388789355
Min length4

Characters and Unicode

Total characters42033394
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

Unique77 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowPeru
3rd rowArgentina
4th rowVenezuela
5th rowSouth Africa
ValueCountFrequency (%)
united 1351629
21.1%
states 1342475
21.0%
brazil 299921
 
4.7%
mexico 290196
 
4.5%
colombia 165032
 
2.6%
venezuela 119690
 
1.9%
peru 116267
 
1.8%
canada 113187
 
1.8%
china 108364
 
1.7%
ecuador 89292
 
1.4%
Other values (309) 2403309
37.6%
2025-03-04T14:37:49.579249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5086492
12.1%
t 4552689
 
10.8%
e 4466872
 
10.6%
i 3789420
 
9.0%
n 3120571
 
7.4%
d 1943611
 
4.6%
1922385
 
4.6%
s 1851829
 
4.4%
S 1538403
 
3.7%
U 1421487
 
3.4%
Other values (55) 12339635
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42033394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5086492
12.1%
t 4552689
 
10.8%
e 4466872
 
10.6%
i 3789420
 
9.0%
n 3120571
 
7.4%
d 1943611
 
4.6%
1922385
 
4.6%
s 1851829
 
4.4%
S 1538403
 
3.7%
U 1421487
 
3.4%
Other values (55) 12339635
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42033394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5086492
12.1%
t 4552689
 
10.8%
e 4466872
 
10.6%
i 3789420
 
9.0%
n 3120571
 
7.4%
d 1943611
 
4.6%
1922385
 
4.6%
s 1851829
 
4.4%
S 1538403
 
3.7%
U 1421487
 
3.4%
Other values (55) 12339635
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42033394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5086492
12.1%
t 4552689
 
10.8%
e 4466872
 
10.6%
i 3789420
 
9.0%
n 3120571
 
7.4%
d 1943611
 
4.6%
1922385
 
4.6%
s 1851829
 
4.4%
S 1538403
 
3.7%
U 1421487
 
3.4%
Other values (55) 12339635
29.4%

countryCode
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:49.616282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
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

Unique1 ?
Unique (%)100.0%

Sample

1st row1872
ValueCountFrequency (%)
1872 1
100.0%
2025-03-04T14:37:49.696455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1
25.0%
8 1
25.0%
7 1
25.0%
2 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
25.0%
8 1
25.0%
7 1
25.0%
2 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
25.0%
8 1
25.0%
7 1
25.0%
2 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
25.0%
8 1
25.0%
7 1
25.0%
2 1
25.0%

stateProvince
Text

Missing 

Distinct4563
Distinct (%)0.1%
Missing1002183
Missing (%)22.2%
Memory size34.5 MiB
2025-03-04T14:37:49.830370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length51
Mean length9.008130405
Min length1

Characters and Unicode

Total characters31649868
Distinct characters144
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

Unique1056 ?
Unique (%)< 0.1%

Sample

1st rowFlorida
2nd rowPiura
3rd rowFormosa
4th rowCarabobo
5th rowManabí
ValueCountFrequency (%)
california 201997
 
4.4%
new 105413
 
2.3%
florida 88714
 
1.9%
virginia 72232
 
1.6%
texas 71235
 
1.5%
alaska 67186
 
1.5%
amazonas 60909
 
1.3%
hawaii 55246
 
1.2%
san 50578
 
1.1%
arizona 50395
 
1.1%
Other values (3812) 3798781
82.2%
2025-03-04T14:37:50.049106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4911736
15.5%
i 2591410
 
8.2%
n 2327641
 
7.4%
o 2312232
 
7.3%
r 2011322
 
6.4%
e 1596527
 
5.0%
s 1274100
 
4.0%
l 1254624
 
4.0%
t 1112877
 
3.5%
1109208
 
3.5%
Other values (134) 11148191
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31649868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4911736
15.5%
i 2591410
 
8.2%
n 2327641
 
7.4%
o 2312232
 
7.3%
r 2011322
 
6.4%
e 1596527
 
5.0%
s 1274100
 
4.0%
l 1254624
 
4.0%
t 1112877
 
3.5%
1109208
 
3.5%
Other values (134) 11148191
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31649868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4911736
15.5%
i 2591410
 
8.2%
n 2327641
 
7.4%
o 2312232
 
7.3%
r 2011322
 
6.4%
e 1596527
 
5.0%
s 1274100
 
4.0%
l 1254624
 
4.0%
t 1112877
 
3.5%
1109208
 
3.5%
Other values (134) 11148191
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31649868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4911736
15.5%
i 2591410
 
8.2%
n 2327641
 
7.4%
o 2312232
 
7.3%
r 2011322
 
6.4%
e 1596527
 
5.0%
s 1274100
 
4.0%
l 1254624
 
4.0%
t 1112877
 
3.5%
1109208
 
3.5%
Other values (134) 11148191
35.2%

county
Text

Missing 

Distinct12276
Distinct (%)1.7%
Missing3778676
Missing (%)83.7%
Memory size34.5 MiB
2025-03-04T14:37:50.314768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length49
Mean length9.15335726
Min length1

Characters and Unicode

Total characters6745887
Distinct characters111
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

Unique3590 ?
Unique (%)0.5%

Sample

1st rowParroquia
2nd rowDuval
3rd rowBoulder
4th rowCantal
5th rowArlington
ValueCountFrequency (%)
county 55931
 
5.4%
san 32851
 
3.2%
prince 19199
 
1.9%
honolulu 19178
 
1.8%
santa 18068
 
1.7%
los 14015
 
1.4%
angeles 13830
 
1.3%
montgomery 13792
 
1.3%
george's 13723
 
1.3%
maui 12914
 
1.2%
Other values (9383) 823511
79.4%
2025-03-04T14:37:50.528788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 769422
 
11.4%
o 546391
 
8.1%
n 533923
 
7.9%
e 515856
 
7.6%
r 422472
 
6.3%
i 392542
 
5.8%
t 309386
 
4.6%
u 307150
 
4.6%
300027
 
4.4%
l 289660
 
4.3%
Other values (101) 2359058
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6745887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 769422
 
11.4%
o 546391
 
8.1%
n 533923
 
7.9%
e 515856
 
7.6%
r 422472
 
6.3%
i 392542
 
5.8%
t 309386
 
4.6%
u 307150
 
4.6%
300027
 
4.4%
l 289660
 
4.3%
Other values (101) 2359058
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6745887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 769422
 
11.4%
o 546391
 
8.1%
n 533923
 
7.9%
e 515856
 
7.6%
r 422472
 
6.3%
i 392542
 
5.8%
t 309386
 
4.6%
u 307150
 
4.6%
300027
 
4.4%
l 289660
 
4.3%
Other values (101) 2359058
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6745887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 769422
 
11.4%
o 546391
 
8.1%
n 533923
 
7.9%
e 515856
 
7.6%
r 422472
 
6.3%
i 392542
 
5.8%
t 309386
 
4.6%
u 307150
 
4.6%
300027
 
4.4%
l 289660
 
4.3%
Other values (101) 2359058
35.0%

locality
Text

Missing 

Distinct2190202
Distinct (%)52.4%
Missing332028
Missing (%)7.4%
Memory size34.5 MiB
2025-03-04T14:37:51.028452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length239546
Median length438
Mean length47.49715737
Min length1

Characters and Unicode

Total characters198710675
Distinct characters424
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

Unique1771025 ?
Unique (%)42.3%

Sample

1st rowGulf of Mexico
2nd rowDept. Piura: Ayabaca
3rd rowDep. Pilcomayo. al E a 2 Km de P. Porteño.
4th rowSelva siempre verde en las quebradas al norte de Los Tanques, arriba de la Planta Eléctrica, en las cabeceras del Río San Gián, al sur de Borburata.
5th rowFlat terrain near Skukuza rest camp, Kruger National Park.
ValueCountFrequency (%)
of 1585417
 
5.0%
de 609558
 
1.9%
the 376944
 
1.2%
km 372123
 
1.2%
near 341319
 
1.1%
and 273717
 
0.9%
on 273627
 
0.9%
in 261704
 
0.8%
county 254734
 
0.8%
la 230318
 
0.7%
Other values (599581) 26894148
85.4%
2025-03-04T14:37:51.577663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27250345
 
13.7%
a 18162578
 
9.1%
e 14022740
 
7.1%
o 13213905
 
6.6%
n 11023738
 
5.5%
i 10220547
 
5.1%
r 10113701
 
5.1%
t 8788622
 
4.4%
l 7107480
 
3.6%
s 6902603
 
3.5%
Other values (414) 71904416
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 198710675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27250345
 
13.7%
a 18162578
 
9.1%
e 14022740
 
7.1%
o 13213905
 
6.6%
n 11023738
 
5.5%
i 10220547
 
5.1%
r 10113701
 
5.1%
t 8788622
 
4.4%
l 7107480
 
3.6%
s 6902603
 
3.5%
Other values (414) 71904416
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 198710675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27250345
 
13.7%
a 18162578
 
9.1%
e 14022740
 
7.1%
o 13213905
 
6.6%
n 11023738
 
5.5%
i 10220547
 
5.1%
r 10113701
 
5.1%
t 8788622
 
4.4%
l 7107480
 
3.6%
s 6902603
 
3.5%
Other values (414) 71904416
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 198710675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27250345
 
13.7%
a 18162578
 
9.1%
e 14022740
 
7.1%
o 13213905
 
6.6%
n 11023738
 
5.5%
i 10220547
 
5.1%
r 10113701
 
5.1%
t 8788622
 
4.4%
l 7107480
 
3.6%
s 6902603
 
3.5%
Other values (414) 71904416
36.2%

verbatimLocality
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing4515656
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:51.617152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.4
Min length10

Characters and Unicode

Total characters52
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

Unique5 ?
Unique (%)100.0%

Sample

1st row83 41'11"W
2nd row78 50' 50" W
3rd row82 44'01"W
4th row82 43'07"W
5th row83 37'47"W
ValueCountFrequency (%)
83 2
16.7%
50 2
16.7%
82 2
16.7%
41'11"w 1
8.3%
78 1
8.3%
w 1
8.3%
44'01"w 1
8.3%
43'07"w 1
8.3%
37'47"w 1
8.3%
2025-03-04T14:37:51.709537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
13.5%
8 5
9.6%
4 5
9.6%
' 5
9.6%
" 5
9.6%
W 5
9.6%
3 4
7.7%
1 4
7.7%
7 4
7.7%
0 4
7.7%
Other values (2) 4
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7
13.5%
8 5
9.6%
4 5
9.6%
' 5
9.6%
" 5
9.6%
W 5
9.6%
3 4
7.7%
1 4
7.7%
7 4
7.7%
0 4
7.7%
Other values (2) 4
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7
13.5%
8 5
9.6%
4 5
9.6%
' 5
9.6%
" 5
9.6%
W 5
9.6%
3 4
7.7%
1 4
7.7%
7 4
7.7%
0 4
7.7%
Other values (2) 4
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7
13.5%
8 5
9.6%
4 5
9.6%
' 5
9.6%
" 5
9.6%
W 5
9.6%
3 4
7.7%
1 4
7.7%
7 4
7.7%
0 4
7.7%
Other values (2) 4
7.7%
Distinct4970
Distinct (%)0.3%
Missing2860984
Missing (%)63.4%
Memory size34.5 MiB
2025-03-04T14:37:51.846769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length9
Mean length5.339273465
Min length3

Characters and Unicode

Total characters8834773
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

Unique773 ?
Unique (%)< 0.1%

Sample

1st row2742.0
2nd row750.0
3rd row50.0
4th row0.0
5th row17.0
ValueCountFrequency (%)
100.0 34114
 
2.1%
1000.0 33132
 
2.0%
200.0 29727
 
1.8%
300.0 26699
 
1.6%
500.0 26609
 
1.6%
1500.0 25888
 
1.6%
800.0 25536
 
1.5%
400.0 23967
 
1.4%
900.0 23544
 
1.4%
1200.0 23149
 
1.4%
Other values (4932) 1382314
83.5%
2025-03-04T14:37:52.066067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3504011
39.7%
. 1654676
18.7%
1 806932
 
9.1%
2 627849
 
7.1%
5 506466
 
5.7%
3 406488
 
4.6%
4 318940
 
3.6%
6 271625
 
3.1%
8 261398
 
3.0%
7 254146
 
2.9%
Other values (17) 222242
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8834773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3504011
39.7%
. 1654676
18.7%
1 806932
 
9.1%
2 627849
 
7.1%
5 506466
 
5.7%
3 406488
 
4.6%
4 318940
 
3.6%
6 271625
 
3.1%
8 261398
 
3.0%
7 254146
 
2.9%
Other values (17) 222242
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8834773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3504011
39.7%
. 1654676
18.7%
1 806932
 
9.1%
2 627849
 
7.1%
5 506466
 
5.7%
3 406488
 
4.6%
4 318940
 
3.6%
6 271625
 
3.1%
8 261398
 
3.0%
7 254146
 
2.9%
Other values (17) 222242
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8834773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3504011
39.7%
. 1654676
18.7%
1 806932
 
9.1%
2 627849
 
7.1%
5 506466
 
5.7%
3 406488
 
4.6%
4 318940
 
3.6%
6 271625
 
3.1%
8 261398
 
3.0%
7 254146
 
2.9%
Other values (17) 222242
 
2.5%
Distinct2856
Distinct (%)0.6%
Missing4017410
Missing (%)89.0%
Memory size34.5 MiB
2025-03-04T14:37:52.223710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.395878784
Min length3

Characters and Unicode

Total characters2688502
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

Unique706 ?
Unique (%)0.1%

Sample

1st row450.0
2nd row850.0
3rd row792.0
4th row1680.0
5th row1981.0
ValueCountFrequency (%)
1000.0 11909
 
2.4%
600.0 10801
 
2.2%
500.0 10617
 
2.1%
1500.0 10106
 
2.0%
900.0 9847
 
2.0%
1200.0 9380
 
1.9%
100.0 9062
 
1.8%
400.0 8956
 
1.8%
300.0 8472
 
1.7%
2000.0 8255
 
1.7%
Other values (2842) 400846
80.5%
2025-03-04T14:37:52.433696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1146584
42.6%
. 498251
18.5%
1 224425
 
8.3%
2 185816
 
6.9%
5 156368
 
5.8%
3 121709
 
4.5%
4 85324
 
3.2%
6 75371
 
2.8%
8 70381
 
2.6%
7 65922
 
2.5%
Other values (2) 58351
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2688502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1146584
42.6%
. 498251
18.5%
1 224425
 
8.3%
2 185816
 
6.9%
5 156368
 
5.8%
3 121709
 
4.5%
4 85324
 
3.2%
6 75371
 
2.8%
8 70381
 
2.6%
7 65922
 
2.5%
Other values (2) 58351
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2688502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1146584
42.6%
. 498251
18.5%
1 224425
 
8.3%
2 185816
 
6.9%
5 156368
 
5.8%
3 121709
 
4.5%
4 85324
 
3.2%
6 75371
 
2.8%
8 70381
 
2.6%
7 65922
 
2.5%
Other values (2) 58351
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2688502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1146584
42.6%
. 498251
18.5%
1 224425
 
8.3%
2 185816
 
6.9%
5 156368
 
5.8%
3 121709
 
4.5%
4 85324
 
3.2%
6 75371
 
2.8%
8 70381
 
2.6%
7 65922
 
2.5%
Other values (2) 58351
 
2.2%

minimumDepthInMeters
Text

Missing 

Distinct172
Distinct (%)0.4%
Missing4475632
Missing (%)99.1%
Memory size34.5 MiB
2025-03-04T14:37:52.486748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.485947688
Min length3

Characters and Unicode

Total characters139539
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

Unique61 ?
Unique (%)0.2%

Sample

1st row3.0
2nd row21.0
3rd row3.0
4th row9.0
5th row3.0
ValueCountFrequency (%)
3.0 7621
19.0%
9.0 6771
16.9%
15.0 6023
15.0%
21.0 4195
10.5%
0.0 2654
 
6.6%
37.0 2182
 
5.5%
27.0 1874
 
4.7%
2.0 1319
 
3.3%
12.0 1049
 
2.6%
1.0 878
 
2.2%
Other values (161) 5463
13.6%
2025-03-04T14:37:52.577794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43497
31.2%
. 40029
28.7%
1 14301
 
10.2%
3 10509
 
7.5%
2 9006
 
6.5%
5 7321
 
5.2%
9 7125
 
5.1%
7 4678
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43497
31.2%
. 40029
28.7%
1 14301
 
10.2%
3 10509
 
7.5%
2 9006
 
6.5%
5 7321
 
5.2%
9 7125
 
5.1%
7 4678
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43497
31.2%
. 40029
28.7%
1 14301
 
10.2%
3 10509
 
7.5%
2 9006
 
6.5%
5 7321
 
5.2%
9 7125
 
5.1%
7 4678
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43497
31.2%
. 40029
28.7%
1 14301
 
10.2%
3 10509
 
7.5%
2 9006
 
6.5%
5 7321
 
5.2%
9 7125
 
5.1%
7 4678
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

maximumDepthInMeters
Text

Missing 

Distinct80
Distinct (%)0.2%
Missing4478965
Missing (%)99.2%
Memory size34.5 MiB
2025-03-04T14:37:52.617109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.677785045
Min length3

Characters and Unicode

Total characters134960
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

Unique19 ?
Unique (%)0.1%

Sample

1st row3.0
2nd row27.0
3rd row3.0
4th row15.0
5th row9.0
ValueCountFrequency (%)
9.0 5795
15.8%
15.0 5742
15.6%
21.0 5321
14.5%
27.0 4190
11.4%
3.0 3061
8.3%
49.0 1873
 
5.1%
37.0 1753
 
4.8%
14.0 1250
 
3.4%
11.0 1072
 
2.9%
5.0 1049
 
2.9%
Other values (70) 5590
15.2%
2025-03-04T14:37:52.716336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37406
27.7%
. 36696
27.2%
1 16747
12.4%
2 10958
 
8.1%
9 7822
 
5.8%
5 7226
 
5.4%
7 6969
 
5.2%
3 5009
 
3.7%
4 3586
 
2.7%
6 1850
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37406
27.7%
. 36696
27.2%
1 16747
12.4%
2 10958
 
8.1%
9 7822
 
5.8%
5 7226
 
5.4%
7 6969
 
5.2%
3 5009
 
3.7%
4 3586
 
2.7%
6 1850
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37406
27.7%
. 36696
27.2%
1 16747
12.4%
2 10958
 
8.1%
9 7822
 
5.8%
5 7226
 
5.4%
7 6969
 
5.2%
3 5009
 
3.7%
4 3586
 
2.7%
6 1850
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37406
27.7%
. 36696
27.2%
1 16747
12.4%
2 10958
 
8.1%
9 7822
 
5.8%
5 7226
 
5.4%
7 6969
 
5.2%
3 5009
 
3.7%
4 3586
 
2.7%
6 1850
 
1.4%

verbatimDepth
Text

Missing 

Distinct18
Distinct (%)0.1%
Missing4494022
Missing (%)99.5%
Memory size34.5 MiB
2025-03-04T14:37:52.747530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length37
Median length3
Mean length3.033874024
Min length2

Characters and Unicode

Total characters65650
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

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowca.
2nd rowca.
3rd rowca.
4th rowca.
5th rowca.
ValueCountFrequency (%)
ca 21589
99.4%
intertidal 57
 
0.3%
mlw 15
 
0.1%
infralittoral 12
 
0.1%
below 6
 
< 0.1%
above 5
 
< 0.1%
low 5
 
< 0.1%
tide 5
 
< 0.1%
feet 2
 
< 0.1%
cay 2
 
< 0.1%
Other values (20) 22
 
0.1%
2025-03-04T14:37:52.835511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 21682
33.0%
c 21591
32.9%
. 21426
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 21682
33.0%
c 21591
32.9%
. 21426
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 21682
33.0%
c 21591
32.9%
. 21426
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 21682
33.0%
c 21591
32.9%
. 21426
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

decimalLatitude
Text

Missing 

Distinct65348
Distinct (%)9.8%
Missing3845453
Missing (%)85.2%
Memory size34.5 MiB
2025-03-04T14:37:52.985586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length55
Median length30
Mean length5.797518084
Min length3

Characters and Unicode

Total characters3885543
Distinct characters39
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

Unique29342 ?
Unique (%)4.4%

Sample

1st row26.2786
2nd row-35.57
3rd row18.6519
4th row-36.68
5th row5.86667
ValueCountFrequency (%)
38.895 3805
 
0.6%
38.9694 3780
 
0.6%
3.61 1737
 
0.3%
0.83 1704
 
0.3%
9.405 1696
 
0.3%
5.16667 1640
 
0.2%
0.35 1588
 
0.2%
38.8664 1571
 
0.2%
5.2 1487
 
0.2%
12.83 1407
 
0.2%
Other values (59827) 649801
97.0%
2025-03-04T14:37:53.212061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 670206
17.2%
3 456711
11.8%
1 360359
9.3%
8 332357
8.6%
2 329986
8.5%
5 313169
8.1%
6 300506
7.7%
7 272005
7.0%
4 239301
 
6.2%
9 228861
 
5.9%
Other values (29) 382082
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3885543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 670206
17.2%
3 456711
11.8%
1 360359
9.3%
8 332357
8.6%
2 329986
8.5%
5 313169
8.1%
6 300506
7.7%
7 272005
7.0%
4 239301
 
6.2%
9 228861
 
5.9%
Other values (29) 382082
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3885543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 670206
17.2%
3 456711
11.8%
1 360359
9.3%
8 332357
8.6%
2 329986
8.5%
5 313169
8.1%
6 300506
7.7%
7 272005
7.0%
4 239301
 
6.2%
9 228861
 
5.9%
Other values (29) 382082
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3885543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 670206
17.2%
3 456711
11.8%
1 360359
9.3%
8 332357
8.6%
2 329986
8.5%
5 313169
8.1%
6 300506
7.7%
7 272005
7.0%
4 239301
 
6.2%
9 228861
 
5.9%
Other values (29) 382082
9.8%

decimalLongitude
Text

Missing 

Distinct67040
Distinct (%)10.0%
Missing3845454
Missing (%)85.2%
Memory size34.5 MiB
2025-03-04T14:37:53.378283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.786640545
Min length3

Characters and Unicode

Total characters4548454
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

Unique27805 ?
Unique (%)4.1%

Sample

1st row-83.7803
2nd row137.32
3rd row-71.5572
4th row-72.97
5th row-60.5667
ValueCountFrequency (%)
77.0367 3741
 
0.6%
77.1767 3712
 
0.6%
59.4833 2407
 
0.4%
53.2 1746
 
0.3%
79.8635 1622
 
0.2%
52.33 1591
 
0.2%
77.7064 1461
 
0.2%
59.48 1420
 
0.2%
70.95 1409
 
0.2%
88.08 1385
 
0.2%
Other values (62110) 649714
96.9%
2025-03-04T14:37:53.605350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 670205
14.7%
- 569111
12.5%
7 494921
10.9%
1 399449
8.8%
6 383274
8.4%
5 381184
8.4%
8 322689
7.1%
3 322209
7.1%
9 271258
6.0%
2 262807
 
5.8%
Other values (18) 471347
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4548454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 670205
14.7%
- 569111
12.5%
7 494921
10.9%
1 399449
8.8%
6 383274
8.4%
5 381184
8.4%
8 322689
7.1%
3 322209
7.1%
9 271258
6.0%
2 262807
 
5.8%
Other values (18) 471347
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4548454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 670205
14.7%
- 569111
12.5%
7 494921
10.9%
1 399449
8.8%
6 383274
8.4%
5 381184
8.4%
8 322689
7.1%
3 322209
7.1%
9 271258
6.0%
2 262807
 
5.8%
Other values (18) 471347
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4548454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 670205
14.7%
- 569111
12.5%
7 494921
10.9%
1 399449
8.8%
6 383274
8.4%
5 381184
8.4%
8 322689
7.1%
3 322209
7.1%
9 271258
6.0%
2 262807
 
5.8%
Other values (18) 471347
10.4%

geodeticDatum
Text

Missing 

Distinct13
Distinct (%)< 0.1%
Missing4485859
Missing (%)99.3%
Memory size34.5 MiB
2025-03-04T14:37:53.653820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length14.73068922
Min length4

Characters and Unicode

Total characters439004
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowWGS84
2nd rowWGS 84 (EPSG:4326)
3rd rowWGS84
4th rowWGS 84 (EPSG:4326)
5th rowWGS 84 (EPSG:4326)
ValueCountFrequency (%)
wgs 20853
28.3%
84 20853
28.3%
epsg:4326 20842
28.2%
wgs84 6659
 
9.0%
not 1679
 
2.3%
recorded 1679
 
2.3%
nad83 385
 
0.5%
epsg:4269 385
 
0.5%
epsg:4267 212
 
0.3%
nad27 212
 
0.3%
Other values (8) 24
 
< 0.1%
2025-03-04T14:37:53.747923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 48970
11.2%
G 48969
11.2%
S 48960
11.2%
43981
10.0%
8 27912
 
6.4%
W 27512
 
6.3%
2 21661
 
4.9%
( 21448
 
4.9%
E 21448
 
4.9%
P 21448
 
4.9%
Other values (21) 106695
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 439004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 48970
11.2%
G 48969
11.2%
S 48960
11.2%
43981
10.0%
8 27912
 
6.4%
W 27512
 
6.3%
2 21661
 
4.9%
( 21448
 
4.9%
E 21448
 
4.9%
P 21448
 
4.9%
Other values (21) 106695
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 439004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 48970
11.2%
G 48969
11.2%
S 48960
11.2%
43981
10.0%
8 27912
 
6.4%
W 27512
 
6.3%
2 21661
 
4.9%
( 21448
 
4.9%
E 21448
 
4.9%
P 21448
 
4.9%
Other values (21) 106695
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 439004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 48970
11.2%
G 48969
11.2%
S 48960
11.2%
43981
10.0%
8 27912
 
6.4%
W 27512
 
6.3%
2 21661
 
4.9%
( 21448
 
4.9%
E 21448
 
4.9%
P 21448
 
4.9%
Other values (21) 106695
24.3%
Distinct21
Distinct (%)0.3%
Missing4509192
Missing (%)99.9%
Memory size34.5 MiB
2025-03-04T14:37:53.785509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.866439944
Min length1

Characters and Unicode

Total characters25012
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 row16000
2nd row1500
3rd row250
4th row500
5th row1500
ValueCountFrequency (%)
16000 1334
20.6%
1000 1304
20.2%
500 1065
16.5%
3000 624
9.6%
250 622
9.6%
750 305
 
4.7%
5000 282
 
4.4%
1500 267
 
4.1%
2000 202
 
3.1%
3500 148
 
2.3%
Other values (11) 316
 
4.9%
2025-03-04T14:37:53.883015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15790
63.1%
1 3052
 
12.2%
5 2771
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15790
63.1%
1 3052
 
12.2%
5 2771
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15790
63.1%
1 3052
 
12.2%
5 2771
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15790
63.1%
1 3052
 
12.2%
5 2771
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

coordinatePrecision
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:53.914114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
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

Unique3 ?
Unique (%)100.0%

Sample

1st row315
2nd row112
3rd row151
ValueCountFrequency (%)
315 1
33.3%
112 1
33.3%
151 1
33.3%
2025-03-04T14:37:53.993910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
55.6%
5 2
 
22.2%
3 1
 
11.1%
2 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5
55.6%
5 2
 
22.2%
3 1
 
11.1%
2 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5
55.6%
5 2
 
22.2%
3 1
 
11.1%
2 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5
55.6%
5 2
 
22.2%
3 1
 
11.1%
2 1
 
11.1%

pointRadiusSpatialFit
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing4515656
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:54.024729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length3
Mean length6.2
Min length3

Characters and Unicode

Total characters31
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

Unique5 ?
Unique (%)100.0%

Sample

1st row315
2nd rowUnited States
3rd rowIndonesia
4th row112
5th row151
ValueCountFrequency (%)
315 1
16.7%
united 1
16.7%
states 1
16.7%
indonesia 1
16.7%
112 1
16.7%
151 1
16.7%
2025-03-04T14:37:54.108824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
16.1%
n 3
9.7%
t 3
9.7%
e 3
9.7%
5 2
 
6.5%
i 2
 
6.5%
d 2
 
6.5%
a 2
 
6.5%
s 2
 
6.5%
3 1
 
3.2%
Other values (6) 6
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5
16.1%
n 3
9.7%
t 3
9.7%
e 3
9.7%
5 2
 
6.5%
i 2
 
6.5%
d 2
 
6.5%
a 2
 
6.5%
s 2
 
6.5%
3 1
 
3.2%
Other values (6) 6
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5
16.1%
n 3
9.7%
t 3
9.7%
e 3
9.7%
5 2
 
6.5%
i 2
 
6.5%
d 2
 
6.5%
a 2
 
6.5%
s 2
 
6.5%
3 1
 
3.2%
Other values (6) 6
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5
16.1%
n 3
9.7%
t 3
9.7%
e 3
9.7%
5 2
 
6.5%
i 2
 
6.5%
d 2
 
6.5%
a 2
 
6.5%
s 2
 
6.5%
3 1
 
3.2%
Other values (6) 6
19.4%

verbatimCoordinates
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing4515657
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:54.139572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row1938
2nd row1988
3rd row1883
4th row1907
ValueCountFrequency (%)
1938 1
25.0%
1988 1
25.0%
1883 1
25.0%
1907 1
25.0%
2025-03-04T14:37:54.217397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 5
31.2%
1 4
25.0%
9 3
18.8%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 5
31.2%
1 4
25.0%
9 3
18.8%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 5
31.2%
1 4
25.0%
9 3
18.8%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 5
31.2%
1 4
25.0%
9 3
18.8%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%

verbatimLatitude
Text

Missing 

Distinct5653
Distinct (%)14.9%
Missing4477670
Missing (%)99.2%
Memory size34.5 MiB
2025-03-04T14:37:54.346333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length66
Median length28
Mean length8.207575478
Min length1

Characters and Unicode

Total characters311814
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

Unique2975 ?
Unique (%)7.8%

Sample

1st row26 16'43"N
2nd row24 58.74' N
3rd row55 56'N
4th row24 47'31"N
5th row19.75856
ValueCountFrequency (%)
n 20751
23.6%
0 7933
 
9.0%
26 2620
 
3.0%
24 2619
 
3.0%
18 2373
 
2.7%
16 2094
 
2.4%
9 1686
 
1.9%
25 1666
 
1.9%
s 1527
 
1.7%
2.2228 1275
 
1.5%
Other values (4210) 43266
49.3%
2025-03-04T14:37:54.557265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49819
16.0%
N 29862
9.6%
2 27458
8.8%
3 24694
 
7.9%
1 23158
 
7.4%
0 22687
 
7.3%
4 21103
 
6.8%
5 17039
 
5.5%
6 15718
 
5.0%
' 15536
 
5.0%
Other values (48) 64740
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 311814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
49819
16.0%
N 29862
9.6%
2 27458
8.8%
3 24694
 
7.9%
1 23158
 
7.4%
0 22687
 
7.3%
4 21103
 
6.8%
5 17039
 
5.5%
6 15718
 
5.0%
' 15536
 
5.0%
Other values (48) 64740
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 311814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
49819
16.0%
N 29862
9.6%
2 27458
8.8%
3 24694
 
7.9%
1 23158
 
7.4%
0 22687
 
7.3%
4 21103
 
6.8%
5 17039
 
5.5%
6 15718
 
5.0%
' 15536
 
5.0%
Other values (48) 64740
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 311814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
49819
16.0%
N 29862
9.6%
2 27458
8.8%
3 24694
 
7.9%
1 23158
 
7.4%
0 22687
 
7.3%
4 21103
 
6.8%
5 17039
 
5.5%
6 15718
 
5.0%
' 15536
 
5.0%
Other values (48) 64740
20.8%

verbatimLongitude
Text

Missing 

Distinct5598
Distinct (%)14.7%
Missing4477686
Missing (%)99.2%
Memory size34.5 MiB
2025-03-04T14:37:54.697637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length29
Mean length8.566662278
Min length1

Characters and Unicode

Total characters325319
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

Unique2941 ?
Unique (%)7.7%

Sample

1st row83 46'49"W
2nd row76 12.75' W
3rd row11 55'E
4th row83 41'11"W
5th row-97.63925
ValueCountFrequency (%)
w 11522
 
13.1%
e 10550
 
12.0%
0 7784
 
8.8%
82 2500
 
2.8%
88 1824
 
2.1%
79 1618
 
1.8%
83 1559
 
1.8%
51 1335
 
1.5%
9.91722 1275
 
1.4%
48.5 1155
 
1.3%
Other values (4274) 46881
53.3%
2025-03-04T14:37:54.905381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50028
15.4%
0 24928
 
7.7%
1 24695
 
7.6%
7 23700
 
7.3%
W 20627
 
6.3%
2 19465
 
6.0%
4 19360
 
6.0%
5 19032
 
5.9%
8 18801
 
5.8%
3 16059
 
4.9%
Other values (41) 88624
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
50028
15.4%
0 24928
 
7.7%
1 24695
 
7.6%
7 23700
 
7.3%
W 20627
 
6.3%
2 19465
 
6.0%
4 19360
 
6.0%
5 19032
 
5.9%
8 18801
 
5.8%
3 16059
 
4.9%
Other values (41) 88624
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
50028
15.4%
0 24928
 
7.7%
1 24695
 
7.6%
7 23700
 
7.3%
W 20627
 
6.3%
2 19465
 
6.0%
4 19360
 
6.0%
5 19032
 
5.9%
8 18801
 
5.8%
3 16059
 
4.9%
Other values (41) 88624
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
50028
15.4%
0 24928
 
7.7%
1 24695
 
7.6%
7 23700
 
7.3%
W 20627
 
6.3%
2 19465
 
6.0%
4 19360
 
6.0%
5 19032
 
5.9%
8 18801
 
5.8%
3 16059
 
4.9%
Other values (41) 88624
27.2%
Distinct6
Distinct (%)< 0.1%
Missing4478628
Missing (%)99.2%
Memory size34.5 MiB
2025-03-04T14:37:55.029170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.98007183
Min length4

Characters and Unicode

Total characters851021
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 37003
33.3%
minutes 36978
33.3%
seconds 36978
33.3%
decimal 25
 
< 0.1%
quad 22
 
< 0.1%
unknown 6
 
< 0.1%
11 1
 
< 0.1%
nov 1
 
< 0.1%
1938 1
 
< 0.1%
21 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
2025-03-04T14:37:55.125040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 184990
21.7%
s 110959
13.0%
73985
 
8.7%
n 73974
 
8.7%
D 37025
 
4.4%
r 37004
 
4.3%
i 37003
 
4.3%
c 37003
 
4.3%
d 37003
 
4.3%
g 37003
 
4.3%
Other values (21) 185072
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 851021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 184990
21.7%
s 110959
13.0%
73985
 
8.7%
n 73974
 
8.7%
D 37025
 
4.4%
r 37004
 
4.3%
i 37003
 
4.3%
c 37003
 
4.3%
d 37003
 
4.3%
g 37003
 
4.3%
Other values (21) 185072
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 851021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 184990
21.7%
s 110959
13.0%
73985
 
8.7%
n 73974
 
8.7%
D 37025
 
4.4%
r 37004
 
4.3%
i 37003
 
4.3%
c 37003
 
4.3%
d 37003
 
4.3%
g 37003
 
4.3%
Other values (21) 185072
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 851021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 184990
21.7%
s 110959
13.0%
73985
 
8.7%
n 73974
 
8.7%
D 37025
 
4.4%
r 37004
 
4.3%
i 37003
 
4.3%
c 37003
 
4.3%
d 37003
 
4.3%
g 37003
 
4.3%
Other values (21) 185072
21.7%

verbatimSRS
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:55.155332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowSan Francisco
ValueCountFrequency (%)
san 1
50.0%
francisco 1
50.0%
2025-03-04T14:37:55.234151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
15.4%
n 2
15.4%
c 2
15.4%
S 1
7.7%
1
7.7%
F 1
7.7%
r 1
7.7%
i 1
7.7%
s 1
7.7%
o 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
15.4%
n 2
15.4%
c 2
15.4%
S 1
7.7%
1
7.7%
F 1
7.7%
r 1
7.7%
i 1
7.7%
s 1
7.7%
o 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
15.4%
n 2
15.4%
c 2
15.4%
S 1
7.7%
1
7.7%
F 1
7.7%
r 1
7.7%
i 1
7.7%
s 1
7.7%
o 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
15.4%
n 2
15.4%
c 2
15.4%
S 1
7.7%
1
7.7%
F 1
7.7%
r 1
7.7%
i 1
7.7%
s 1
7.7%
o 1
7.7%

footprintSpatialFit
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:55.265879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length19
Mean length23
Min length17

Characters and Unicode

Total characters69
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowAsplenium monanthes
2nd rowNymphoides indica
3rd rowKohleria inaequalis var. lindenii
ValueCountFrequency (%)
asplenium 1
12.5%
monanthes 1
12.5%
nymphoides 1
12.5%
indica 1
12.5%
kohleria 1
12.5%
inaequalis 1
12.5%
var 1
12.5%
lindenii 1
12.5%
2025-03-04T14:37:55.357038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 10
14.5%
n 7
 
10.1%
a 6
 
8.7%
e 6
 
8.7%
5
 
7.2%
l 4
 
5.8%
s 4
 
5.8%
h 3
 
4.3%
o 3
 
4.3%
m 3
 
4.3%
Other values (13) 18
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 10
14.5%
n 7
 
10.1%
a 6
 
8.7%
e 6
 
8.7%
5
 
7.2%
l 4
 
5.8%
s 4
 
5.8%
h 3
 
4.3%
o 3
 
4.3%
m 3
 
4.3%
Other values (13) 18
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 10
14.5%
n 7
 
10.1%
a 6
 
8.7%
e 6
 
8.7%
5
 
7.2%
l 4
 
5.8%
s 4
 
5.8%
h 3
 
4.3%
o 3
 
4.3%
m 3
 
4.3%
Other values (13) 18
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 10
14.5%
n 7
 
10.1%
a 6
 
8.7%
e 6
 
8.7%
5
 
7.2%
l 4
 
5.8%
s 4
 
5.8%
h 3
 
4.3%
o 3
 
4.3%
m 3
 
4.3%
Other values (13) 18
26.1%

georeferenceProtocol
Text

Missing 

Distinct20
Distinct (%)< 0.1%
Missing4388537
Missing (%)97.2%
Memory size34.5 MiB
2025-03-04T14:37:55.391242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length8.340360593
Min length3

Characters and Unicode

Total characters1060260
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGazetteer
2nd rowGazetteer
3rd rowGazetteer
4th rowGazetteer
5th rowLabel
ValueCountFrequency (%)
gazetteer 49173
29.7%
gps 23394
14.1%
gis 20969
12.7%
arcview 20969
12.7%
label 17064
 
10.3%
google 15524
 
9.4%
maps 12428
 
7.5%
earth 3096
 
1.9%
source 1688
 
1.0%
g-1 397
 
0.2%
Other values (11) 704
 
0.4%
2025-03-04T14:37:55.483304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 203052
19.2%
G 109414
 
10.3%
t 101502
 
9.6%
a 82086
 
7.7%
r 74984
 
7.1%
z 49173
 
4.6%
S 45993
 
4.3%
38282
 
3.6%
o 32954
 
3.1%
l 32699
 
3.1%
Other values (30) 290121
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1060260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 203052
19.2%
G 109414
 
10.3%
t 101502
 
9.6%
a 82086
 
7.7%
r 74984
 
7.1%
z 49173
 
4.6%
S 45993
 
4.3%
38282
 
3.6%
o 32954
 
3.1%
l 32699
 
3.1%
Other values (30) 290121
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1060260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 203052
19.2%
G 109414
 
10.3%
t 101502
 
9.6%
a 82086
 
7.7%
r 74984
 
7.1%
z 49173
 
4.6%
S 45993
 
4.3%
38282
 
3.6%
o 32954
 
3.1%
l 32699
 
3.1%
Other values (30) 290121
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1060260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 203052
19.2%
G 109414
 
10.3%
t 101502
 
9.6%
a 82086
 
7.7%
r 74984
 
7.1%
z 49173
 
4.6%
S 45993
 
4.3%
38282
 
3.6%
o 32954
 
3.1%
l 32699
 
3.1%
Other values (30) 290121
27.4%

georeferenceRemarks
Text

Missing 

Distinct78
Distinct (%)15.3%
Missing4515150
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:55.552493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length67
Median length57
Mean length19.70254403
Min length1

Characters and Unicode

Total characters10068
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

Unique29 ?
Unique (%)5.7%

Sample

1st row+-1000m
2nd rowstop 1 - beginning of bike path, along GW pkwy
3rd rowca.; ca.
4th rowstop 1-ditch; stop 2- polkweed; stop 3; stop 4
5th rowLong. 4 8 W - 4 15 W
ValueCountFrequency (%)
stop 226
 
10.5%
4 144
 
6.7%
119
 
5.5%
w 106
 
4.9%
ca 90
 
4.2%
1 86
 
4.0%
seconds 56
 
2.6%
long 53
 
2.5%
15 53
 
2.5%
8 53
 
2.5%
Other values (116) 1167
54.2%
2025-03-04T14:37:55.701891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

geologicalContextID
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing4515657
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:55.743857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length49
Mean length49
Min length42

Characters and Unicode

Total characters196
Distinct characters30
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 (%)100.0%

Sample

1st rowSouth America - Neotropics, Colombia, Meta
2nd rowSouth America - Neotropics, Ecuador, Morona-Santiago
3rd rowNorth America, United States, California, San Bernardino
4th rowNorth America, United States, Arizona, Cochise
ValueCountFrequency (%)
america 4
16.0%
south 2
 
8.0%
2
 
8.0%
neotropics 2
 
8.0%
north 2
 
8.0%
united 2
 
8.0%
states 2
 
8.0%
colombia 1
 
4.0%
meta 1
 
4.0%
ecuador 1
 
4.0%
Other values (6) 6
24.0%
2025-03-04T14:37:55.846930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
10.7%
o 18
 
9.2%
a 17
 
8.7%
i 15
 
7.7%
t 14
 
7.1%
r 14
 
7.1%
e 13
 
6.6%
, 10
 
5.1%
n 9
 
4.6%
c 8
 
4.1%
Other values (20) 57
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21
 
10.7%
o 18
 
9.2%
a 17
 
8.7%
i 15
 
7.7%
t 14
 
7.1%
r 14
 
7.1%
e 13
 
6.6%
, 10
 
5.1%
n 9
 
4.6%
c 8
 
4.1%
Other values (20) 57
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21
 
10.7%
o 18
 
9.2%
a 17
 
8.7%
i 15
 
7.7%
t 14
 
7.1%
r 14
 
7.1%
e 13
 
6.6%
, 10
 
5.1%
n 9
 
4.6%
c 8
 
4.1%
Other values (20) 57
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21
 
10.7%
o 18
 
9.2%
a 17
 
8.7%
i 15
 
7.7%
t 14
 
7.1%
r 14
 
7.1%
e 13
 
6.6%
, 10
 
5.1%
n 9
 
4.6%
c 8
 
4.1%
Other values (20) 57
29.1%
Distinct5
Distinct (%)71.4%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:55.886182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length49
Median length48
Mean length31.57142857
Min length13

Characters and Unicode

Total characters221
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

Unique3 ?
Unique (%)42.9%

Sample

1st rowSouth America - Neotropics
2nd rowPlantae, Pteridophyte, Polypodiales, Aspleniaceae
3rd rowPlantae, Dicotyledonae, Asterales, Menyanthaceae
4th rowPlantae, Dicotyledonae, Lamiales, Gesneriaceae
5th rowSouth America - Neotropics
ValueCountFrequency (%)
america 4
16.7%
plantae 3
12.5%
south 2
8.3%
2
8.3%
neotropics 2
8.3%
north 2
8.3%
dicotyledonae 2
8.3%
pteridophyte 1
 
4.2%
polypodiales 1
 
4.2%
aspleniaceae 1
 
4.2%
Other values (4) 4
16.7%
2025-03-04T14:37:55.983805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 29
13.1%
a 23
 
10.4%
17
 
7.7%
o 15
 
6.8%
t 15
 
6.8%
i 13
 
5.9%
c 11
 
5.0%
r 11
 
5.0%
l 10
 
4.5%
, 9
 
4.1%
Other values (17) 68
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29
13.1%
a 23
 
10.4%
17
 
7.7%
o 15
 
6.8%
t 15
 
6.8%
i 13
 
5.9%
c 11
 
5.0%
r 11
 
5.0%
l 10
 
4.5%
, 9
 
4.1%
Other values (17) 68
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29
13.1%
a 23
 
10.4%
17
 
7.7%
o 15
 
6.8%
t 15
 
6.8%
i 13
 
5.9%
c 11
 
5.0%
r 11
 
5.0%
l 10
 
4.5%
, 9
 
4.1%
Other values (17) 68
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29
13.1%
a 23
 
10.4%
17
 
7.7%
o 15
 
6.8%
t 15
 
6.8%
i 13
 
5.9%
c 11
 
5.0%
r 11
 
5.0%
l 10
 
4.5%
, 9
 
4.1%
Other values (17) 68
30.8%
Distinct5
Distinct (%)71.4%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.019266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length13
Mean length12.14285714
Min length7

Characters and Unicode

Total characters85
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

Unique4 ?
Unique (%)57.1%

Sample

1st rowAlgae name updating Project
2nd rowEarle, S. A.
3rd rowPlantae
4th rowPlantae
5th rowBlair, S. M.
ValueCountFrequency (%)
plantae 3
18.8%
s 2
12.5%
algae 1
 
6.2%
name 1
 
6.2%
updating 1
 
6.2%
project 1
 
6.2%
earle 1
 
6.2%
a 1
 
6.2%
blair 1
 
6.2%
m 1
 
6.2%
Other values (3) 3
18.8%
2025-03-04T14:37:56.119752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11
12.9%
9
 
10.6%
e 8
 
9.4%
n 7
 
8.2%
l 6
 
7.1%
. 6
 
7.1%
t 5
 
5.9%
P 4
 
4.7%
, 3
 
3.5%
r 3
 
3.5%
Other values (17) 23
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11
12.9%
9
 
10.6%
e 8
 
9.4%
n 7
 
8.2%
l 6
 
7.1%
. 6
 
7.1%
t 5
 
5.9%
P 4
 
4.7%
, 3
 
3.5%
r 3
 
3.5%
Other values (17) 23
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11
12.9%
9
 
10.6%
e 8
 
9.4%
n 7
 
8.2%
l 6
 
7.1%
. 6
 
7.1%
t 5
 
5.9%
P 4
 
4.7%
, 3
 
3.5%
r 3
 
3.5%
Other values (17) 23
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11
12.9%
9
 
10.6%
e 8
 
9.4%
n 7
 
8.2%
l 6
 
7.1%
. 6
 
7.1%
t 5
 
5.9%
P 4
 
4.7%
, 3
 
3.5%
r 3
 
3.5%
Other values (17) 23
27.1%
Distinct2
Distinct (%)66.7%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.150700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.66666667
Min length12

Characters and Unicode

Total characters38
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

Unique1 ?
Unique (%)33.3%

Sample

1st rowPteridophyte
2nd rowDicotyledonae
3rd rowDicotyledonae
ValueCountFrequency (%)
dicotyledonae 2
66.7%
pteridophyte 1
33.3%
2025-03-04T14:37:56.237799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6
15.8%
o 5
13.2%
t 4
10.5%
i 3
7.9%
y 3
7.9%
d 3
7.9%
D 2
 
5.3%
c 2
 
5.3%
l 2
 
5.3%
n 2
 
5.3%
Other values (5) 6
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6
15.8%
o 5
13.2%
t 4
10.5%
i 3
7.9%
y 3
7.9%
d 3
7.9%
D 2
 
5.3%
c 2
 
5.3%
l 2
 
5.3%
n 2
 
5.3%
Other values (5) 6
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6
15.8%
o 5
13.2%
t 4
10.5%
i 3
7.9%
y 3
7.9%
d 3
7.9%
D 2
 
5.3%
c 2
 
5.3%
l 2
 
5.3%
n 2
 
5.3%
Other values (5) 6
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6
15.8%
o 5
13.2%
t 4
10.5%
i 3
7.9%
y 3
7.9%
d 3
7.9%
D 2
 
5.3%
c 2
 
5.3%
l 2
 
5.3%
n 2
 
5.3%
Other values (5) 6
15.8%
Distinct6
Distinct (%)85.7%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.270513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10
Min length7

Characters and Unicode

Total characters70
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)71.4%

Sample

1st rowColombia
2nd rowPolypodiales
3rd rowAsterales
4th rowLamiales
5th rowEcuador
ValueCountFrequency (%)
united 2
22.2%
states 2
22.2%
colombia 1
11.1%
polypodiales 1
11.1%
asterales 1
11.1%
lamiales 1
11.1%
ecuador 1
11.1%
2025-03-04T14:37:56.365760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8
11.4%
a 8
11.4%
t 7
 
10.0%
s 6
 
8.6%
l 5
 
7.1%
o 5
 
7.1%
i 5
 
7.1%
d 4
 
5.7%
r 2
 
2.9%
m 2
 
2.9%
Other values (14) 18
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8
11.4%
a 8
11.4%
t 7
 
10.0%
s 6
 
8.6%
l 5
 
7.1%
o 5
 
7.1%
i 5
 
7.1%
d 4
 
5.7%
r 2
 
2.9%
m 2
 
2.9%
Other values (14) 18
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8
11.4%
a 8
11.4%
t 7
 
10.0%
s 6
 
8.6%
l 5
 
7.1%
o 5
 
7.1%
i 5
 
7.1%
d 4
 
5.7%
r 2
 
2.9%
m 2
 
2.9%
Other values (14) 18
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8
11.4%
a 8
11.4%
t 7
 
10.0%
s 6
 
8.6%
l 5
 
7.1%
o 5
 
7.1%
i 5
 
7.1%
d 4
 
5.7%
r 2
 
2.9%
m 2
 
2.9%
Other values (14) 18
25.7%
Distinct7
Distinct (%)100.0%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.400711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length12
Mean length10.42857143
Min length4

Characters and Unicode

Total characters73
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowMeta
2nd rowAspleniaceae
3rd rowMenyanthaceae
4th rowGesneriaceae
5th rowMorona-Santiago
ValueCountFrequency (%)
meta 1
14.3%
aspleniaceae 1
14.3%
menyanthaceae 1
14.3%
gesneriaceae 1
14.3%
morona-santiago 1
14.3%
california 1
14.3%
arizona 1
14.3%
2025-03-04T14:37:56.489543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14
19.2%
e 11
15.1%
n 8
11.0%
i 6
8.2%
o 5
 
6.8%
r 4
 
5.5%
M 3
 
4.1%
t 3
 
4.1%
c 3
 
4.1%
A 2
 
2.7%
Other values (12) 14
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14
19.2%
e 11
15.1%
n 8
11.0%
i 6
8.2%
o 5
 
6.8%
r 4
 
5.5%
M 3
 
4.1%
t 3
 
4.1%
c 3
 
4.1%
A 2
 
2.7%
Other values (12) 14
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14
19.2%
e 11
15.1%
n 8
11.0%
i 6
8.2%
o 5
 
6.8%
r 4
 
5.5%
M 3
 
4.1%
t 3
 
4.1%
c 3
 
4.1%
A 2
 
2.7%
Other values (12) 14
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14
19.2%
e 11
15.1%
n 8
11.0%
i 6
8.2%
o 5
 
6.8%
r 4
 
5.5%
M 3
 
4.1%
t 3
 
4.1%
c 3
 
4.1%
A 2
 
2.7%
Other values (12) 14
19.2%
Distinct2
Distinct (%)100.0%
Missing4515659
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.522024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length10.5
Mean length10.5
Min length7

Characters and Unicode

Total characters21
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowSan Bernardino
2nd rowCochise
ValueCountFrequency (%)
san 1
33.3%
bernardino 1
33.3%
cochise 1
33.3%
2025-03-04T14:37:56.611121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 3
14.3%
a 2
9.5%
e 2
9.5%
r 2
9.5%
i 2
9.5%
o 2
9.5%
S 1
 
4.8%
1
 
4.8%
B 1
 
4.8%
d 1
 
4.8%
Other values (4) 4
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 3
14.3%
a 2
9.5%
e 2
9.5%
r 2
9.5%
i 2
9.5%
o 2
9.5%
S 1
 
4.8%
1
 
4.8%
B 1
 
4.8%
d 1
 
4.8%
Other values (4) 4
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 3
14.3%
a 2
9.5%
e 2
9.5%
r 2
9.5%
i 2
9.5%
o 2
9.5%
S 1
 
4.8%
1
 
4.8%
B 1
 
4.8%
d 1
 
4.8%
Other values (4) 4
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 3
14.3%
a 2
9.5%
e 2
9.5%
r 2
9.5%
i 2
9.5%
o 2
9.5%
S 1
 
4.8%
1
 
4.8%
B 1
 
4.8%
d 1
 
4.8%
Other values (4) 4
19.0%
Distinct4
Distinct (%)100.0%
Missing4515657
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.653756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length84
Median length46
Mean length47.75
Min length15

Characters and Unicode

Total characters191
Distinct characters37
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 (%)100.0%

Sample

1st rowVilla Vicencia.
2nd rowMorona-Santiago: Cordillera del Boliche; about 60 km from Limon south to Gualaquiza.
3rd rowSouthern California. Lugonia. San Berdo Co.
4th rowChiricahua Mountains, Barfoot Park, stony knolls.
ValueCountFrequency (%)
villa 1
 
3.8%
vicencia 1
 
3.8%
stony 1
 
3.8%
park 1
 
3.8%
barfoot 1
 
3.8%
mountains 1
 
3.8%
chiricahua 1
 
3.8%
co 1
 
3.8%
berdo 1
 
3.8%
san 1
 
3.8%
Other values (16) 16
61.5%
2025-03-04T14:37:56.755527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
11.5%
o 20
 
10.5%
a 19
 
9.9%
i 14
 
7.3%
n 12
 
6.3%
r 10
 
5.2%
l 10
 
5.2%
u 8
 
4.2%
t 8
 
4.2%
e 6
 
3.1%
Other values (27) 62
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22
 
11.5%
o 20
 
10.5%
a 19
 
9.9%
i 14
 
7.3%
n 12
 
6.3%
r 10
 
5.2%
l 10
 
5.2%
u 8
 
4.2%
t 8
 
4.2%
e 6
 
3.1%
Other values (27) 62
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22
 
11.5%
o 20
 
10.5%
a 19
 
9.9%
i 14
 
7.3%
n 12
 
6.3%
r 10
 
5.2%
l 10
 
5.2%
u 8
 
4.2%
t 8
 
4.2%
e 6
 
3.1%
Other values (27) 62
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22
 
11.5%
o 20
 
10.5%
a 19
 
9.9%
i 14
 
7.3%
n 12
 
6.3%
r 10
 
5.2%
l 10
 
5.2%
u 8
 
4.2%
t 8
 
4.2%
e 6
 
3.1%
Other values (27) 62
32.5%
Distinct3
Distinct (%)100.0%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.788086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9
Min length8

Characters and Unicode

Total characters27
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

Unique3 ?
Unique (%)100.0%

Sample

1st rowAsplenium
2nd rowNymphoides
3rd rowKohleria
ValueCountFrequency (%)
asplenium 1
33.3%
nymphoides 1
33.3%
kohleria 1
33.3%
2025-03-04T14:37:56.881078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3
11.1%
i 3
11.1%
m 2
 
7.4%
h 2
 
7.4%
p 2
 
7.4%
l 2
 
7.4%
s 2
 
7.4%
o 2
 
7.4%
r 1
 
3.7%
K 1
 
3.7%
Other values (7) 7
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3
11.1%
i 3
11.1%
m 2
 
7.4%
h 2
 
7.4%
p 2
 
7.4%
l 2
 
7.4%
s 2
 
7.4%
o 2
 
7.4%
r 1
 
3.7%
K 1
 
3.7%
Other values (7) 7
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3
11.1%
i 3
11.1%
m 2
 
7.4%
h 2
 
7.4%
p 2
 
7.4%
l 2
 
7.4%
s 2
 
7.4%
o 2
 
7.4%
r 1
 
3.7%
K 1
 
3.7%
Other values (7) 7
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3
11.1%
i 3
11.1%
m 2
 
7.4%
h 2
 
7.4%
p 2
 
7.4%
l 2
 
7.4%
s 2
 
7.4%
o 2
 
7.4%
r 1
 
3.7%
K 1
 
3.7%
Other values (7) 7
25.9%
Distinct3
Distinct (%)100.0%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:56.910108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.666666667
Min length5

Characters and Unicode

Total characters17
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row500.0
2nd row1650.0
3rd row2438.0
ValueCountFrequency (%)
500.0 1
33.3%
1650.0 1
33.3%
2438.0 1
33.3%
2025-03-04T14:37:56.992944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
5 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%
2 1
 
5.9%
4 1
 
5.9%
3 1
 
5.9%
8 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
5 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%
2 1
 
5.9%
4 1
 
5.9%
3 1
 
5.9%
8 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
5 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%
2 1
 
5.9%
4 1
 
5.9%
3 1
 
5.9%
8 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
5 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%
2 1
 
5.9%
4 1
 
5.9%
3 1
 
5.9%
8 1
 
5.9%

lithostratigraphicTerms
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:57.022969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
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

Unique1 ?
Unique (%)100.0%

Sample

1st row500.0
ValueCountFrequency (%)
500.0 1
100.0%
2025-03-04T14:37:57.104956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3
60.0%
5 1
 
20.0%
. 1
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3
60.0%
5 1
 
20.0%
. 1
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3
60.0%
5 1
 
20.0%
. 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3
60.0%
5 1
 
20.0%
. 1
 
20.0%

formation
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:57.134397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.333333333
Min length6

Characters and Unicode

Total characters25
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowmonanthes
2nd rowindica
3rd rowinaequalis
ValueCountFrequency (%)
monanthes 1
33.3%
indica 1
33.3%
inaequalis 1
33.3%
2025-03-04T14:37:57.223799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 4
16.0%
a 4
16.0%
i 4
16.0%
e 2
8.0%
s 2
8.0%
m 1
 
4.0%
o 1
 
4.0%
t 1
 
4.0%
h 1
 
4.0%
d 1
 
4.0%
Other values (4) 4
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 4
16.0%
a 4
16.0%
i 4
16.0%
e 2
8.0%
s 2
8.0%
m 1
 
4.0%
o 1
 
4.0%
t 1
 
4.0%
h 1
 
4.0%
d 1
 
4.0%
Other values (4) 4
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 4
16.0%
a 4
16.0%
i 4
16.0%
e 2
8.0%
s 2
8.0%
m 1
 
4.0%
o 1
 
4.0%
t 1
 
4.0%
h 1
 
4.0%
d 1
 
4.0%
Other values (4) 4
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 4
16.0%
a 4
16.0%
i 4
16.0%
e 2
8.0%
s 2
8.0%
m 1
 
4.0%
o 1
 
4.0%
t 1
 
4.0%
h 1
 
4.0%
d 1
 
4.0%
Other values (4) 4
16.0%

member
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:57.258940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length19
Mean length19.28571429
Min length8

Characters and Unicode

Total characters135
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

Unique7 ?
Unique (%)100.0%

Sample

1st rowRinorea pubiflora var. pubiflora
2nd rowAgissea simulans
3rd rowColpomenia sinuosa
4th rowLaurencia intricata
5th rowAvrainvillea nigricans
ValueCountFrequency (%)
pubiflora 2
13.3%
rinorea 1
 
6.7%
var 1
 
6.7%
agissea 1
 
6.7%
simulans 1
 
6.7%
colpomenia 1
 
6.7%
sinuosa 1
 
6.7%
laurencia 1
 
6.7%
intricata 1
 
6.7%
avrainvillea 1
 
6.7%
Other values (4) 4
26.7%
2025-03-04T14:37:57.428122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 20
14.8%
a 16
11.9%
n 14
10.4%
e 10
 
7.4%
s 8
 
5.9%
r 8
 
5.9%
8
 
5.9%
o 7
 
5.2%
l 7
 
5.2%
u 5
 
3.7%
Other values (16) 32
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 20
14.8%
a 16
11.9%
n 14
10.4%
e 10
 
7.4%
s 8
 
5.9%
r 8
 
5.9%
8
 
5.9%
o 7
 
5.2%
l 7
 
5.2%
u 5
 
3.7%
Other values (16) 32
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 20
14.8%
a 16
11.9%
n 14
10.4%
e 10
 
7.4%
s 8
 
5.9%
r 8
 
5.9%
8
 
5.9%
o 7
 
5.2%
l 7
 
5.2%
u 5
 
3.7%
Other values (16) 32
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 20
14.8%
a 16
11.9%
n 14
10.4%
e 10
 
7.4%
s 8
 
5.9%
r 8
 
5.9%
8
 
5.9%
o 7
 
5.2%
l 7
 
5.2%
u 5
 
3.7%
Other values (16) 32
23.7%

bed
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:57.457570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowRiccardia pinguis
ValueCountFrequency (%)
riccardia 1
50.0%
pinguis 1
50.0%
2025-03-04T14:37:57.535394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4
23.5%
c 2
11.8%
a 2
11.8%
R 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
p 1
 
5.9%
n 1
 
5.9%
g 1
 
5.9%
Other values (2) 2
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4
23.5%
c 2
11.8%
a 2
11.8%
R 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
p 1
 
5.9%
n 1
 
5.9%
g 1
 
5.9%
Other values (2) 2
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4
23.5%
c 2
11.8%
a 2
11.8%
R 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
p 1
 
5.9%
n 1
 
5.9%
g 1
 
5.9%
Other values (2) 2
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4
23.5%
c 2
11.8%
a 2
11.8%
R 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
p 1
 
5.9%
n 1
 
5.9%
g 1
 
5.9%
Other values (2) 2
11.8%

identificationID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:57.563406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowVariety
ValueCountFrequency (%)
variety 1
100.0%
2025-03-04T14:37:57.642429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%
Distinct20
Distinct (%)0.2%
Missing4504655
Missing (%)99.8%
Memory size34.5 MiB
2025-03-04T14:37:57.671756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length3
Mean length4.359985462
Min length2

Characters and Unicode

Total characters47986
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowcf.
2nd rowcf.
3rd rowcf.
4th rowvel aff.
5th rowvel aff.
ValueCountFrequency (%)
cf 5895
51.4%
aff 2849
24.9%
uncertain 1610
 
14.0%
s.l 543
 
4.7%
vel 347
 
3.0%
near 76
 
0.7%
sp 64
 
0.6%
nov 42
 
0.4%
s.s 27
 
0.2%
l 2
 
< 0.1%
Other values (7) 7
 
0.1%
2025-03-04T14:37:57.755142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 11593
24.2%
. 9933
20.7%
c 7505
15.6%
a 4536
 
9.5%
n 3340
 
7.0%
e 2034
 
4.2%
r 1686
 
3.5%
t 1613
 
3.4%
i 1611
 
3.4%
u 1601
 
3.3%
Other values (19) 2534
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 11593
24.2%
. 9933
20.7%
c 7505
15.6%
a 4536
 
9.5%
n 3340
 
7.0%
e 2034
 
4.2%
r 1686
 
3.5%
t 1613
 
3.4%
i 1611
 
3.4%
u 1601
 
3.3%
Other values (19) 2534
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 11593
24.2%
. 9933
20.7%
c 7505
15.6%
a 4536
 
9.5%
n 3340
 
7.0%
e 2034
 
4.2%
r 1686
 
3.5%
t 1613
 
3.4%
i 1611
 
3.4%
u 1601
 
3.3%
Other values (19) 2534
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 11593
24.2%
. 9933
20.7%
c 7505
15.6%
a 4536
 
9.5%
n 3340
 
7.0%
e 2034
 
4.2%
r 1686
 
3.5%
t 1613
 
3.4%
i 1611
 
3.4%
u 1601
 
3.3%
Other values (19) 2534
 
5.3%

typeStatus
Text

Missing 

Distinct192
Distinct (%)0.2%
Missing4399315
Missing (%)97.4%
Memory size34.5 MiB
2025-03-04T14:37:57.787777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length7
Mean length8.824067867
Min length4

Characters and Unicode

Total characters1026645
Distinct characters36
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

Unique70 ?
Unique (%)0.1%

Sample

1st rowIsotype
2nd rowIsotype
3rd rowHolotype
4th rowType Collection
5th rowType Collection
ValueCountFrequency (%)
isotype 61603
45.1%
holotype 19964
 
14.6%
type 16845
 
12.3%
collection 9779
 
7.2%
isosyntype 7051
 
5.2%
syntype 6301
 
4.6%
fragment 5516
 
4.0%
isolectotype 2869
 
2.1%
possible 2534
 
1.9%
lectotype 1378
 
1.0%
Other values (16) 2653
 
1.9%
2025-03-04T14:37:57.895164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 141371
13.8%
o 139676
13.6%
y 131094
12.8%
t 121736
11.9%
p 117820
11.5%
s 84712
8.3%
I 71992
7.0%
l 46312
 
4.5%
n 29146
 
2.8%
20147
 
2.0%
Other values (26) 122639
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1026645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141371
13.8%
o 139676
13.6%
y 131094
12.8%
t 121736
11.9%
p 117820
11.5%
s 84712
8.3%
I 71992
7.0%
l 46312
 
4.5%
n 29146
 
2.8%
20147
 
2.0%
Other values (26) 122639
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1026645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141371
13.8%
o 139676
13.6%
y 131094
12.8%
t 121736
11.9%
p 117820
11.5%
s 84712
8.3%
I 71992
7.0%
l 46312
 
4.5%
n 29146
 
2.8%
20147
 
2.0%
Other values (26) 122639
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1026645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141371
13.8%
o 139676
13.6%
y 131094
12.8%
t 121736
11.9%
p 117820
11.5%
s 84712
8.3%
I 71992
7.0%
l 46312
 
4.5%
n 29146
 
2.8%
20147
 
2.0%
Other values (26) 122639
11.9%

identifiedBy
Text

Missing 

Distinct8134
Distinct (%)1.5%
Missing3958097
Missing (%)87.7%
Memory size34.5 MiB
2025-03-04T14:37:58.024657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length131
Median length109
Mean length37.72206957
Min length2

Characters and Unicode

Total characters21032468
Distinct characters98
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

Unique2641 ?
Unique (%)0.5%

Sample

1st rowBlair, S. M.
2nd rowAcevedo-Rodríguez, P., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
3rd rowAcevedo-Rodríguez, P., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
4th rowWagner, W. L., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
5th rowWagner, W. L., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
ValueCountFrequency (%)
united 136541
 
4.2%
states 136495
 
4.2%
of 126287
 
3.8%
123596
 
3.8%
national 120671
 
3.7%
museum 119573
 
3.6%
smithsonian 118957
 
3.6%
natural 118768
 
3.6%
history 118661
 
3.6%
institution 118645
 
3.6%
Other values (6317) 2046516
62.3%
2025-03-04T14:37:58.265457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2727146
 
13.0%
t 1181664
 
5.6%
a 1144451
 
5.4%
o 1117247
 
5.3%
i 1047010
 
5.0%
n 1030069
 
4.9%
, 907900
 
4.3%
. 858290
 
4.1%
r 853980
 
4.1%
e 836095
 
4.0%
Other values (88) 9328616
44.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21032468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2727146
 
13.0%
t 1181664
 
5.6%
a 1144451
 
5.4%
o 1117247
 
5.3%
i 1047010
 
5.0%
n 1030069
 
4.9%
, 907900
 
4.3%
. 858290
 
4.1%
r 853980
 
4.1%
e 836095
 
4.0%
Other values (88) 9328616
44.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21032468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2727146
 
13.0%
t 1181664
 
5.6%
a 1144451
 
5.4%
o 1117247
 
5.3%
i 1047010
 
5.0%
n 1030069
 
4.9%
, 907900
 
4.3%
. 858290
 
4.1%
r 853980
 
4.1%
e 836095
 
4.0%
Other values (88) 9328616
44.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21032468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2727146
 
13.0%
t 1181664
 
5.6%
a 1144451
 
5.4%
o 1117247
 
5.3%
i 1047010
 
5.0%
n 1030069
 
4.9%
, 907900
 
4.3%
. 858290
 
4.1%
r 853980
 
4.1%
e 836095
 
4.0%
Other values (88) 9328616
44.4%

identifiedByID
Text

Missing 

Distinct6
Distinct (%)100.0%
Missing4515655
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:58.328781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length71
Median length65.5
Mean length65.5
Min length59

Characters and Unicode

Total characters393
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

Unique6 ?
Unique (%)100.0%

Sample

1st rowPlantae, Dicotyledonae, Malpighiales, Violaceae, Violoideae
2nd rowPlantae, Rhodophyta, Florideophyceae, Peyssonneliales, Peyssonneliaceae
3rd rowChromista, Ochrophyta, Phaeophyceae, Ectocarpales, Scytosiphonaceae
4th rowPlantae, Rhodophyta, Florideophyceae, Ceramiales, Rhodomelaceae
5th rowPlantae, Chlorophyta, Ulvophyceae, Bryopsidales, Dichotomosiphonaceae
ValueCountFrequency (%)
plantae 5
16.7%
rhodophyta 2
 
6.7%
florideophyceae 2
 
6.7%
ulvophyceae 2
 
6.7%
chlorophyta 2
 
6.7%
ectocarpales 1
 
3.3%
cladophorales 1
 
3.3%
dichotomosiphonaceae 1
 
3.3%
bryopsidales 1
 
3.3%
rhodomelaceae 1
 
3.3%
Other values (12) 12
40.0%
2025-03-04T14:37:58.435698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 47
12.0%
e 47
12.0%
o 37
 
9.4%
l 25
 
6.4%
, 24
 
6.1%
24
 
6.1%
h 23
 
5.9%
c 17
 
4.3%
i 16
 
4.1%
y 16
 
4.1%
Other values (22) 117
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 47
12.0%
e 47
12.0%
o 37
 
9.4%
l 25
 
6.4%
, 24
 
6.1%
24
 
6.1%
h 23
 
5.9%
c 17
 
4.3%
i 16
 
4.1%
y 16
 
4.1%
Other values (22) 117
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 47
12.0%
e 47
12.0%
o 37
 
9.4%
l 25
 
6.4%
, 24
 
6.1%
24
 
6.1%
h 23
 
5.9%
c 17
 
4.3%
i 16
 
4.1%
y 16
 
4.1%
Other values (22) 117
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 47
12.0%
e 47
12.0%
o 37
 
9.4%
l 25
 
6.4%
, 24
 
6.1%
24
 
6.1%
h 23
 
5.9%
c 17
 
4.3%
i 16
 
4.1%
y 16
 
4.1%
Other values (22) 117
29.8%

dateIdentified
Text

Missing 

Distinct3
Distinct (%)42.9%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:58.470158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length69
Median length7
Mean length16.14285714
Min length7

Characters and Unicode

Total characters113
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

Unique2 ?
Unique (%)28.6%

Sample

1st rowPlantae
2nd rowPlantae
3rd rowChromista
4th rowPlantae
5th rowPlantae
ValueCountFrequency (%)
plantae 6
54.5%
chromista 1
 
9.1%
marchantiophyta 1
 
9.1%
jungermanniopsida 1
 
9.1%
metzgeriales 1
 
9.1%
aneuraceae 1
 
9.1%
2025-03-04T14:37:58.567019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 21
18.6%
e 13
11.5%
n 11
9.7%
t 10
 
8.8%
l 7
 
6.2%
P 6
 
5.3%
i 5
 
4.4%
r 5
 
4.4%
4
 
3.5%
, 4
 
3.5%
Other values (15) 27
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 21
18.6%
e 13
11.5%
n 11
9.7%
t 10
 
8.8%
l 7
 
6.2%
P 6
 
5.3%
i 5
 
4.4%
r 5
 
4.4%
4
 
3.5%
, 4
 
3.5%
Other values (15) 27
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 21
18.6%
e 13
11.5%
n 11
9.7%
t 10
 
8.8%
l 7
 
6.2%
P 6
 
5.3%
i 5
 
4.4%
r 5
 
4.4%
4
 
3.5%
, 4
 
3.5%
Other values (15) 27
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 21
18.6%
e 13
11.5%
n 11
9.7%
t 10
 
8.8%
l 7
 
6.2%
P 6
 
5.3%
i 5
 
4.4%
r 5
 
4.4%
4
 
3.5%
, 4
 
3.5%
Other values (15) 27
23.9%
Distinct4
Distinct (%)66.7%
Missing4515655
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:58.598674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length10.5
Mean length9.833333333
Min length7

Characters and Unicode

Total characters59
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

Unique2 ?
Unique (%)33.3%

Sample

1st rowRhodophyta
2nd rowOchrophyta
3rd rowRhodophyta
4th rowChlorophyta
5th rowChlorophyta
ValueCountFrequency (%)
rhodophyta 2
33.3%
chlorophyta 2
33.3%
ochrophyta 1
16.7%
plantae 1
16.7%
2025-03-04T14:37:58.703307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 10
16.9%
o 9
15.3%
a 7
11.9%
t 6
10.2%
p 5
8.5%
y 5
8.5%
l 3
 
5.1%
r 3
 
5.1%
R 2
 
3.4%
d 2
 
3.4%
Other values (6) 7
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 10
16.9%
o 9
15.3%
a 7
11.9%
t 6
10.2%
p 5
8.5%
y 5
8.5%
l 3
 
5.1%
r 3
 
5.1%
R 2
 
3.4%
d 2
 
3.4%
Other values (6) 7
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 10
16.9%
o 9
15.3%
a 7
11.9%
t 6
10.2%
p 5
8.5%
y 5
8.5%
l 3
 
5.1%
r 3
 
5.1%
R 2
 
3.4%
d 2
 
3.4%
Other values (6) 7
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 10
16.9%
o 9
15.3%
a 7
11.9%
t 6
10.2%
p 5
8.5%
y 5
8.5%
l 3
 
5.1%
r 3
 
5.1%
R 2
 
3.4%
d 2
 
3.4%
Other values (6) 7
11.9%
Distinct5
Distinct (%)71.4%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:58.734439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length13.14285714
Min length11

Characters and Unicode

Total characters92
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

Unique3 ?
Unique (%)42.9%

Sample

1st rowDicotyledonae
2nd rowFlorideophyceae
3rd rowPhaeophyceae
4th rowFlorideophyceae
5th rowUlvophyceae
ValueCountFrequency (%)
florideophyceae 2
28.6%
ulvophyceae 2
28.6%
dicotyledonae 1
14.3%
phaeophyceae 1
14.3%
marchantiophyta 1
14.3%
2025-03-04T14:37:58.833745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15
16.3%
o 10
10.9%
a 10
10.9%
h 8
8.7%
y 7
7.6%
c 7
7.6%
p 6
 
6.5%
l 5
 
5.4%
i 4
 
4.3%
t 3
 
3.3%
Other values (9) 17
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15
16.3%
o 10
10.9%
a 10
10.9%
h 8
8.7%
y 7
7.6%
c 7
7.6%
p 6
 
6.5%
l 5
 
5.4%
i 4
 
4.3%
t 3
 
3.3%
Other values (9) 17
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15
16.3%
o 10
10.9%
a 10
10.9%
h 8
8.7%
y 7
7.6%
c 7
7.6%
p 6
 
6.5%
l 5
 
5.4%
i 4
 
4.3%
t 3
 
3.3%
Other values (9) 17
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15
16.3%
o 10
10.9%
a 10
10.9%
h 8
8.7%
y 7
7.6%
c 7
7.6%
p 6
 
6.5%
l 5
 
5.4%
i 4
 
4.3%
t 3
 
3.3%
Other values (9) 17
18.5%

identificationRemarks
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing4515654
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:58.870128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length13
Min length10

Characters and Unicode

Total characters91
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowMalpighiales
2nd rowPeyssonneliales
3rd rowEctocarpales
4th rowCeramiales
5th rowBryopsidales
ValueCountFrequency (%)
malpighiales 1
14.3%
peyssonneliales 1
14.3%
ectocarpales 1
14.3%
ceramiales 1
14.3%
bryopsidales 1
14.3%
cladophorales 1
14.3%
jungermanniopsida 1
14.3%
2025-03-04T14:37:58.969138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12
13.2%
e 10
11.0%
s 10
11.0%
l 9
9.9%
i 7
 
7.7%
o 6
 
6.6%
p 5
 
5.5%
r 5
 
5.5%
n 5
 
5.5%
d 3
 
3.3%
Other values (13) 19
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12
13.2%
e 10
11.0%
s 10
11.0%
l 9
9.9%
i 7
 
7.7%
o 6
 
6.6%
p 5
 
5.5%
r 5
 
5.5%
n 5
 
5.5%
d 3
 
3.3%
Other values (13) 19
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12
13.2%
e 10
11.0%
s 10
11.0%
l 9
9.9%
i 7
 
7.7%
o 6
 
6.6%
p 5
 
5.5%
r 5
 
5.5%
n 5
 
5.5%
d 3
 
3.3%
Other values (13) 19
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12
13.2%
e 10
11.0%
s 10
11.0%
l 9
9.9%
i 7
 
7.7%
o 6
 
6.6%
p 5
 
5.5%
r 5
 
5.5%
n 5
 
5.5%
d 3
 
3.3%
Other values (13) 19
20.9%

taxonID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:58.999713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMetzgeriales
ValueCountFrequency (%)
metzgeriales 1
100.0%
2025-03-04T14:37:59.082868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3
25.0%
M 1
 
8.3%
t 1
 
8.3%
z 1
 
8.3%
g 1
 
8.3%
r 1
 
8.3%
i 1
 
8.3%
a 1
 
8.3%
l 1
 
8.3%
s 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3
25.0%
M 1
 
8.3%
t 1
 
8.3%
z 1
 
8.3%
g 1
 
8.3%
r 1
 
8.3%
i 1
 
8.3%
a 1
 
8.3%
l 1
 
8.3%
s 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3
25.0%
M 1
 
8.3%
t 1
 
8.3%
z 1
 
8.3%
g 1
 
8.3%
r 1
 
8.3%
i 1
 
8.3%
a 1
 
8.3%
l 1
 
8.3%
s 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3
25.0%
M 1
 
8.3%
t 1
 
8.3%
z 1
 
8.3%
g 1
 
8.3%
r 1
 
8.3%
i 1
 
8.3%
a 1
 
8.3%
l 1
 
8.3%
s 1
 
8.3%

scientificNameID
Text

Missing 

Distinct6
Distinct (%)100.0%
Missing4515655
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:59.118661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length15
Mean length14.66666667
Min length9

Characters and Unicode

Total characters88
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

Unique6 ?
Unique (%)100.0%

Sample

1st rowViolaceae
2nd rowPeyssonneliaceae
3rd rowScytosiphonaceae
4th rowRhodomelaceae
5th rowDichotomosiphonaceae
ValueCountFrequency (%)
violaceae 1
16.7%
peyssonneliaceae 1
16.7%
scytosiphonaceae 1
16.7%
rhodomelaceae 1
16.7%
dichotomosiphonaceae 1
16.7%
anadyomenaceae 1
16.7%
2025-03-04T14:37:59.210494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 16
18.2%
a 13
14.8%
o 11
12.5%
c 8
9.1%
n 6
 
6.8%
i 5
 
5.7%
s 4
 
4.5%
h 4
 
4.5%
l 3
 
3.4%
y 3
 
3.4%
Other values (10) 15
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16
18.2%
a 13
14.8%
o 11
12.5%
c 8
9.1%
n 6
 
6.8%
i 5
 
5.7%
s 4
 
4.5%
h 4
 
4.5%
l 3
 
3.4%
y 3
 
3.4%
Other values (10) 15
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16
18.2%
a 13
14.8%
o 11
12.5%
c 8
9.1%
n 6
 
6.8%
i 5
 
5.7%
s 4
 
4.5%
h 4
 
4.5%
l 3
 
3.4%
y 3
 
3.4%
Other values (10) 15
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16
18.2%
a 13
14.8%
o 11
12.5%
c 8
9.1%
n 6
 
6.8%
i 5
 
5.7%
s 4
 
4.5%
h 4
 
4.5%
l 3
 
3.4%
y 3
 
3.4%
Other values (10) 15
17.0%

acceptedNameUsageID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:59.240175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAneuraceae
ValueCountFrequency (%)
aneuraceae 1
100.0%
2025-03-04T14:37:59.320902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3
30.0%
a 2
20.0%
A 1
 
10.0%
n 1
 
10.0%
u 1
 
10.0%
r 1
 
10.0%
c 1
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3
30.0%
a 2
20.0%
A 1
 
10.0%
n 1
 
10.0%
u 1
 
10.0%
r 1
 
10.0%
c 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3
30.0%
a 2
20.0%
A 1
 
10.0%
n 1
 
10.0%
u 1
 
10.0%
r 1
 
10.0%
c 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3
30.0%
a 2
20.0%
A 1
 
10.0%
n 1
 
10.0%
u 1
 
10.0%
r 1
 
10.0%
c 1
 
10.0%

nameAccordingToID
Text

Missing 

Distinct6
Distinct (%)100.0%
Missing4515655
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:59.351389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.166666667
Min length7

Characters and Unicode

Total characters55
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

Unique6 ?
Unique (%)100.0%

Sample

1st rowRinorea
2nd rowAgissea
3rd rowColpomenia
4th rowLaurencia
5th rowAvrainvillea
ValueCountFrequency (%)
rinorea 1
16.7%
agissea 1
16.7%
colpomenia 1
16.7%
laurencia 1
16.7%
avrainvillea 1
16.7%
anadyomene 1
16.7%
2025-03-04T14:37:59.440179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8
14.5%
e 7
12.7%
n 6
10.9%
i 6
10.9%
o 4
 
7.3%
r 3
 
5.5%
A 3
 
5.5%
l 3
 
5.5%
s 2
 
3.6%
v 2
 
3.6%
Other values (10) 11
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8
14.5%
e 7
12.7%
n 6
10.9%
i 6
10.9%
o 4
 
7.3%
r 3
 
5.5%
A 3
 
5.5%
l 3
 
5.5%
s 2
 
3.6%
v 2
 
3.6%
Other values (10) 11
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8
14.5%
e 7
12.7%
n 6
10.9%
i 6
10.9%
o 4
 
7.3%
r 3
 
5.5%
A 3
 
5.5%
l 3
 
5.5%
s 2
 
3.6%
v 2
 
3.6%
Other values (10) 11
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8
14.5%
e 7
12.7%
n 6
10.9%
i 6
10.9%
o 4
 
7.3%
r 3
 
5.5%
A 3
 
5.5%
l 3
 
5.5%
s 2
 
3.6%
v 2
 
3.6%
Other values (10) 11
20.0%

namePublishedInID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:37:59.471165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRiccardia
ValueCountFrequency (%)
riccardia 1
100.0%
2025-03-04T14:37:59.559814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2
22.2%
c 2
22.2%
a 2
22.2%
R 1
11.1%
r 1
11.1%
d 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2
22.2%
c 2
22.2%
a 2
22.2%
R 1
11.1%
r 1
11.1%
d 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2
22.2%
c 2
22.2%
a 2
22.2%
R 1
11.1%
r 1
11.1%
d 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2
22.2%
c 2
22.2%
a 2
22.2%
R 1
11.1%
r 1
11.1%
d 1
11.1%
Distinct330689
Distinct (%)7.3%
Missing13722
Missing (%)0.3%
Memory size34.5 MiB
2025-03-04T14:37:59.737653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length98
Mean length19.78998916
Min length5

Characters and Unicode

Total characters89093324
Distinct characters97
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

Unique121029 ?
Unique (%)2.7%

Sample

1st rowLithothamnion calcareum
2nd rowAmicia glandulosa
3rd rowTripogandra glandulosa
4th rowConnarus steyermarkii
5th rowTrichoneura grandiglumis
ValueCountFrequency (%)
sp 270265
 
2.8%
var 210171
 
2.2%
subsp 106168
 
1.1%
carex 58129
 
0.6%
indet 41306
 
0.4%
poa 30191
 
0.3%
cyperus 27842
 
0.3%
cladonia 27025
 
0.3%
paspalum 26142
 
0.3%
solanum 24852
 
0.3%
Other values (98717) 8921619
91.6%
2025-03-04T14:38:00.002830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10712057
 
12.0%
i 8471292
 
9.5%
e 5756840
 
6.5%
s 5576870
 
6.3%
r 5526161
 
6.2%
5241771
 
5.9%
o 5207769
 
5.8%
l 4893187
 
5.5%
n 4698467
 
5.3%
u 4559281
 
5.1%
Other values (87) 28449629
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89093324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10712057
 
12.0%
i 8471292
 
9.5%
e 5756840
 
6.5%
s 5576870
 
6.3%
r 5526161
 
6.2%
5241771
 
5.9%
o 5207769
 
5.8%
l 4893187
 
5.5%
n 4698467
 
5.3%
u 4559281
 
5.1%
Other values (87) 28449629
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89093324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10712057
 
12.0%
i 8471292
 
9.5%
e 5756840
 
6.5%
s 5576870
 
6.3%
r 5526161
 
6.2%
5241771
 
5.9%
o 5207769
 
5.8%
l 4893187
 
5.5%
n 4698467
 
5.3%
u 4559281
 
5.1%
Other values (87) 28449629
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89093324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10712057
 
12.0%
i 8471292
 
9.5%
e 5756840
 
6.5%
s 5576870
 
6.3%
r 5526161
 
6.2%
5241771
 
5.9%
o 5207769
 
5.8%
l 4893187
 
5.5%
n 4698467
 
5.3%
u 4559281
 
5.1%
Other values (87) 28449629
31.9%

acceptedNameUsage
Text

Missing 

Distinct6
Distinct (%)100.0%
Missing4515655
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:00.047926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.5
Min length7

Characters and Unicode

Total characters51
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

Unique6 ?
Unique (%)100.0%

Sample

1st rowpubiflora
2nd rowsimulans
3rd rowsinuosa
4th rowintricata
5th rownigricans
ValueCountFrequency (%)
pubiflora 1
16.7%
simulans 1
16.7%
sinuosa 1
16.7%
intricata 1
16.7%
nigricans 1
16.7%
menziesii 1
16.7%
2025-03-04T14:38:00.232485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 10
19.6%
a 6
11.8%
s 6
11.8%
n 6
11.8%
r 3
 
5.9%
u 3
 
5.9%
c 2
 
3.9%
e 2
 
3.9%
l 2
 
3.9%
o 2
 
3.9%
Other values (7) 9
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 10
19.6%
a 6
11.8%
s 6
11.8%
n 6
11.8%
r 3
 
5.9%
u 3
 
5.9%
c 2
 
3.9%
e 2
 
3.9%
l 2
 
3.9%
o 2
 
3.9%
Other values (7) 9
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 10
19.6%
a 6
11.8%
s 6
11.8%
n 6
11.8%
r 3
 
5.9%
u 3
 
5.9%
c 2
 
3.9%
e 2
 
3.9%
l 2
 
3.9%
o 2
 
3.9%
Other values (7) 9
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 10
19.6%
a 6
11.8%
s 6
11.8%
n 6
11.8%
r 3
 
5.9%
u 3
 
5.9%
c 2
 
3.9%
e 2
 
3.9%
l 2
 
3.9%
o 2
 
3.9%
Other values (7) 9
17.6%

parentNameUsage
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing4515659
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:00.266058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8
Min length7

Characters and Unicode

Total characters16
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

Unique2 ?
Unique (%)100.0%

Sample

1st rowpubiflora
2nd rowpinguis
ValueCountFrequency (%)
pubiflora 1
50.0%
pinguis 1
50.0%
2025-03-04T14:38:00.357931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 3
18.8%
p 2
12.5%
u 2
12.5%
b 1
 
6.2%
f 1
 
6.2%
l 1
 
6.2%
o 1
 
6.2%
r 1
 
6.2%
a 1
 
6.2%
n 1
 
6.2%
Other values (2) 2
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3
18.8%
p 2
12.5%
u 2
12.5%
b 1
 
6.2%
f 1
 
6.2%
l 1
 
6.2%
o 1
 
6.2%
r 1
 
6.2%
a 1
 
6.2%
n 1
 
6.2%
Other values (2) 2
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3
18.8%
p 2
12.5%
u 2
12.5%
b 1
 
6.2%
f 1
 
6.2%
l 1
 
6.2%
o 1
 
6.2%
r 1
 
6.2%
a 1
 
6.2%
n 1
 
6.2%
Other values (2) 2
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3
18.8%
p 2
12.5%
u 2
12.5%
b 1
 
6.2%
f 1
 
6.2%
l 1
 
6.2%
o 1
 
6.2%
r 1
 
6.2%
a 1
 
6.2%
n 1
 
6.2%
Other values (2) 2
12.5%

nameAccordingTo
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:00.389191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowvariety
ValueCountFrequency (%)
variety 1
100.0%
2025-03-04T14:38:00.469838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 1
14.3%
a 1
14.3%
r 1
14.3%
i 1
14.3%
e 1
14.3%
t 1
14.3%
y 1
14.3%

namePublishedInYear
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing4515656
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:00.506884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length27
Mean length21.6
Min length6

Characters and Unicode

Total characters108
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

Unique5 ?
Unique (%)100.0%

Sample

1st row(Benth.) Sprague & Sandwith
2nd row(Weber Bosse) Pestana et al.
3rd row(K. Mert. ex Roth) Derbes & Solier
4th rowJ.V.Lamouroux
5th rowDecne.
ValueCountFrequency (%)
2
 
11.1%
benth 1
 
5.6%
k 1
 
5.6%
j.v.lamouroux 1
 
5.6%
solier 1
 
5.6%
derbes 1
 
5.6%
roth 1
 
5.6%
ex 1
 
5.6%
mert 1
 
5.6%
al 1
 
5.6%
Other values (7) 7
38.9%
2025-03-04T14:38:00.601026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14
 
13.0%
13
 
12.0%
. 7
 
6.5%
t 6
 
5.6%
r 6
 
5.6%
a 6
 
5.6%
o 5
 
4.6%
n 4
 
3.7%
s 4
 
3.7%
( 3
 
2.8%
Other values (25) 40
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14
 
13.0%
13
 
12.0%
. 7
 
6.5%
t 6
 
5.6%
r 6
 
5.6%
a 6
 
5.6%
o 5
 
4.6%
n 4
 
3.7%
s 4
 
3.7%
( 3
 
2.8%
Other values (25) 40
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14
 
13.0%
13
 
12.0%
. 7
 
6.5%
t 6
 
5.6%
r 6
 
5.6%
a 6
 
5.6%
o 5
 
4.6%
n 4
 
3.7%
s 4
 
3.7%
( 3
 
2.8%
Other values (25) 40
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14
 
13.0%
13
 
12.0%
. 7
 
6.5%
t 6
 
5.6%
r 6
 
5.6%
a 6
 
5.6%
o 5
 
4.6%
n 4
 
3.7%
s 4
 
3.7%
( 3
 
2.8%
Other values (25) 40
37.0%
Distinct2220
Distinct (%)< 0.1%
Missing13879
Missing (%)0.3%
Memory size34.5 MiB
2025-03-04T14:38:00.647675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length135
Median length89
Mean length55.77126436
Min length6

Characters and Unicode

Total characters251070074
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

Unique232 ?
Unique (%)< 0.1%

Sample

1st rowPlantae, Rhodophyta, Corallinales, Lithothamniaceae
2nd rowPlantae, Dicotyledonae, Fabales, Fabaceae, Papilionoideae
3rd rowPlantae, Monocotyledonae, Commelinales, Commelinaceae
4th rowPlantae, Dicotyledonae, Oxalidales, Connaraceae
5th rowPlantae, Monocotyledonae, Poales, Poaceae, Chloridoideae
ValueCountFrequency (%)
plantae 4143724
 
19.6%
dicotyledonae 2583518
 
12.2%
monocotyledonae 909034
 
4.3%
poales 702157
 
3.3%
poaceae 502389
 
2.4%
asterales 380254
 
1.8%
asteraceae 358301
 
1.7%
asteroideae 282993
 
1.3%
pteridophyte 276624
 
1.3%
lamiales 266378
 
1.3%
Other values (2232) 10769135
50.9%
2025-03-04T14:38:00.782110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35371989
14.1%
e 35160991
14.0%
o 18857025
 
7.5%
16672725
 
6.6%
, 16563118
 
6.6%
l 16272824
 
6.5%
n 12770502
 
5.1%
t 12542392
 
5.0%
i 12457371
 
5.0%
c 11339619
 
4.5%
Other values (50) 63061518
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 251070074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35371989
14.1%
e 35160991
14.0%
o 18857025
 
7.5%
16672725
 
6.6%
, 16563118
 
6.6%
l 16272824
 
6.5%
n 12770502
 
5.1%
t 12542392
 
5.0%
i 12457371
 
5.0%
c 11339619
 
4.5%
Other values (50) 63061518
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 251070074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35371989
14.1%
e 35160991
14.0%
o 18857025
 
7.5%
16672725
 
6.6%
, 16563118
 
6.6%
l 16272824
 
6.5%
n 12770502
 
5.1%
t 12542392
 
5.0%
i 12457371
 
5.0%
c 11339619
 
4.5%
Other values (50) 63061518
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 251070074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35371989
14.1%
e 35160991
14.0%
o 18857025
 
7.5%
16672725
 
6.6%
, 16563118
 
6.6%
l 16272824
 
6.5%
n 12770502
 
5.1%
t 12542392
 
5.0%
i 12457371
 
5.0%
c 11339619
 
4.5%
Other values (50) 63061518
25.1%
Distinct13
Distinct (%)< 0.1%
Missing15585
Missing (%)0.3%
Memory size34.5 MiB
2025-03-04T14:38:00.819167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length7
Mean length6.96302618
Min length5

Characters and Unicode

Total characters31334147
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPlantae
2nd rowPlantae
3rd rowPlantae
4th rowPlantae
5th rowPlantae
ValueCountFrequency (%)
plantae 4143685
92.1%
fungi 223269
 
5.0%
eubacteria 52558
 
1.2%
chromista 41845
 
0.9%
protista 38689
 
0.9%
protozoa 24
 
< 0.1%
incertae 3
 
< 0.1%
sedis 3
 
< 0.1%
prokaryota 2
 
< 0.1%
kingdom 2
 
< 0.1%
Other values (4) 4
 
< 0.1%
2025-03-04T14:38:00.908276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8473056
27.0%
n 4366962
13.9%
t 4315498
13.8%
e 4196255
13.4%
P 4182397
13.3%
l 4143685
13.2%
i 356370
 
1.1%
u 275829
 
0.9%
g 223272
 
0.7%
F 223267
 
0.7%
Other values (18) 577556
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31334147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8473056
27.0%
n 4366962
13.9%
t 4315498
13.8%
e 4196255
13.4%
P 4182397
13.3%
l 4143685
13.2%
i 356370
 
1.1%
u 275829
 
0.9%
g 223272
 
0.7%
F 223267
 
0.7%
Other values (18) 577556
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31334147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8473056
27.0%
n 4366962
13.9%
t 4315498
13.8%
e 4196255
13.4%
P 4182397
13.3%
l 4143685
13.2%
i 356370
 
1.1%
u 275829
 
0.9%
g 223272
 
0.7%
F 223267
 
0.7%
Other values (18) 577556
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31334147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8473056
27.0%
n 4366962
13.9%
t 4315498
13.8%
e 4196255
13.4%
P 4182397
13.3%
l 4143685
13.2%
i 356370
 
1.1%
u 275829
 
0.9%
g 223272
 
0.7%
F 223267
 
0.7%
Other values (18) 577556
 
1.8%

phylum
Text

Missing 

Distinct37
Distinct (%)< 0.1%
Missing3795307
Missing (%)84.0%
Memory size34.5 MiB
2025-03-04T14:38:00.942920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length10
Mean length10.45428498
Min length6

Characters and Unicode

Total characters7530786
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowRhodophyta
2nd rowBryophyta
3rd rowAscomycota
4th rowRhodophyta
5th rowBryophyta
ValueCountFrequency (%)
ascomycota 220381
30.6%
bryophyta 148369
20.6%
rhodophyta 121618
16.9%
cyanobacteria 52553
 
7.3%
chlorophyta 44629
 
6.2%
bacillariophyta 33225
 
4.6%
ochrophyta 29634
 
4.1%
marchantiophyta 26552
 
3.7%
pinophyta 21234
 
2.9%
miozoa 4849
 
0.7%
Other values (29) 18257
 
2.5%
2025-03-04T14:38:01.028561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1114609
14.8%
a 950427
12.6%
y 866328
11.5%
t 747357
9.9%
h 664707
8.8%
c 586886
7.8%
p 438109
 
5.8%
r 348688
 
4.6%
s 224255
 
3.0%
m 222302
 
3.0%
Other values (26) 1367118
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7530786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1114609
14.8%
a 950427
12.6%
y 866328
11.5%
t 747357
9.9%
h 664707
8.8%
c 586886
7.8%
p 438109
 
5.8%
r 348688
 
4.6%
s 224255
 
3.0%
m 222302
 
3.0%
Other values (26) 1367118
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7530786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1114609
14.8%
a 950427
12.6%
y 866328
11.5%
t 747357
9.9%
h 664707
8.8%
c 586886
7.8%
p 438109
 
5.8%
r 348688
 
4.6%
s 224255
 
3.0%
m 222302
 
3.0%
Other values (26) 1367118
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7530786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1114609
14.8%
a 950427
12.6%
y 866328
11.5%
t 747357
9.9%
h 664707
8.8%
c 586886
7.8%
p 438109
 
5.8%
r 348688
 
4.6%
s 224255
 
3.0%
m 222302
 
3.0%
Other values (26) 1367118
18.2%

class
Text

Missing 

Distinct88
Distinct (%)< 0.1%
Missing166450
Missing (%)3.7%
Memory size34.5 MiB
2025-03-04T14:38:01.060804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length13
Mean length13.51310502
Min length6

Characters and Unicode

Total characters58771345
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

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowDicotyledonae
2nd rowMonocotyledonae
3rd rowDicotyledonae
4th rowMonocotyledonae
5th rowDicotyledonae
ValueCountFrequency (%)
dicotyledonae 2583517
58.2%
monocotyledonae 909034
 
20.5%
pteridophyte 276624
 
6.2%
lecanoromycetes 202991
 
4.6%
bryopsida 127668
 
2.9%
florideophyceae 89454
 
2.0%
basal 85188
 
1.9%
ulvophyceae 35522
 
0.8%
jungermanniopsida 25865
 
0.6%
pinopsida 23209
 
0.5%
Other values (80) 76533
 
1.7%
2025-03-04T14:38:01.153956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 9980172
17.0%
e 8651381
14.7%
n 4733378
8.1%
t 4293923
7.3%
y 4289084
7.3%
a 4288463
7.3%
c 4090012
7.0%
d 4070361
6.9%
l 3713365
 
6.3%
i 3238121
 
5.5%
Other values (37) 7423085
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58771345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 9980172
17.0%
e 8651381
14.7%
n 4733378
8.1%
t 4293923
7.3%
y 4289084
7.3%
a 4288463
7.3%
c 4090012
7.0%
d 4070361
6.9%
l 3713365
 
6.3%
i 3238121
 
5.5%
Other values (37) 7423085
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58771345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 9980172
17.0%
e 8651381
14.7%
n 4733378
8.1%
t 4293923
7.3%
y 4289084
7.3%
a 4288463
7.3%
c 4090012
7.0%
d 4070361
6.9%
l 3713365
 
6.3%
i 3238121
 
5.5%
Other values (37) 7423085
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58771345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 9980172
17.0%
e 8651381
14.7%
n 4733378
8.1%
t 4293923
7.3%
y 4289084
7.3%
a 4288463
7.3%
c 4090012
7.0%
d 4070361
6.9%
l 3713365
 
6.3%
i 3238121
 
5.5%
Other values (37) 7423085
12.6%

order
Text

Missing 

Distinct404
Distinct (%)< 0.1%
Missing53019
Missing (%)1.2%
Memory size34.5 MiB
2025-03-04T14:38:01.279971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length31
Mean length9.299688615
Min length6

Characters and Unicode

Total characters41501181
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

Unique39 ?
Unique (%)< 0.1%

Sample

1st rowCorallinales
2nd rowFabales
3rd rowCommelinales
4th rowOxalidales
5th rowPoales
ValueCountFrequency (%)
poales 702157
 
15.7%
asterales 380253
 
8.5%
lamiales 266378
 
6.0%
fabales 254592
 
5.7%
malpighiales 211605
 
4.7%
polypodiales 193095
 
4.3%
gentianales 180579
 
4.0%
myrtales 158308
 
3.5%
caryophyllales 147659
 
3.3%
ericales 129764
 
2.9%
Other values (398) 1839979
41.2%
2025-03-04T14:38:01.487883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6796977
16.4%
l 5858220
14.1%
e 5561572
13.4%
s 5364064
12.9%
i 2260328
 
5.4%
o 2043726
 
4.9%
r 1726800
 
4.2%
n 1206408
 
2.9%
t 1060667
 
2.6%
P 1016591
 
2.4%
Other values (41) 8605828
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41501181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6796977
16.4%
l 5858220
14.1%
e 5561572
13.4%
s 5364064
12.9%
i 2260328
 
5.4%
o 2043726
 
4.9%
r 1726800
 
4.2%
n 1206408
 
2.9%
t 1060667
 
2.6%
P 1016591
 
2.4%
Other values (41) 8605828
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41501181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6796977
16.4%
l 5858220
14.1%
e 5561572
13.4%
s 5364064
12.9%
i 2260328
 
5.4%
o 2043726
 
4.9%
r 1726800
 
4.2%
n 1206408
 
2.9%
t 1060667
 
2.6%
P 1016591
 
2.4%
Other values (41) 8605828
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41501181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6796977
16.4%
l 5858220
14.1%
e 5561572
13.4%
s 5364064
12.9%
i 2260328
 
5.4%
o 2043726
 
4.9%
r 1726800
 
4.2%
n 1206408
 
2.9%
t 1060667
 
2.6%
P 1016591
 
2.4%
Other values (41) 8605828
20.7%

family
Text

Missing 

Distinct1349
Distinct (%)< 0.1%
Missing49040
Missing (%)1.1%
Memory size34.5 MiB
2025-03-04T14:38:01.630619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length34
Mean length10.77015601
Min length6

Characters and Unicode

Total characters48106205
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

Unique107 ?
Unique (%)< 0.1%

Sample

1st rowLithothamniaceae
2nd rowFabaceae
3rd rowCommelinaceae
4th rowConnaraceae
5th rowPoaceae
ValueCountFrequency (%)
poaceae 502389
 
11.2%
asteraceae 358301
 
8.0%
fabaceae 237916
 
5.3%
cyperaceae 139791
 
3.1%
rubiaceae 119885
 
2.7%
melastomataceae 73747
 
1.6%
parmeliaceae 66931
 
1.5%
rosaceae 65723
 
1.5%
lamiaceae 62361
 
1.4%
euphorbiaceae 59298
 
1.3%
Other values (1336) 2800225
62.4%
2025-03-04T14:38:01.841532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10978354
22.8%
e 10624096
22.1%
c 5308142
11.0%
i 2196406
 
4.6%
r 2091808
 
4.3%
o 2020260
 
4.2%
l 1542594
 
3.2%
t 1385215
 
2.9%
n 1300651
 
2.7%
s 1002425
 
2.1%
Other values (47) 9656254
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48106205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10978354
22.8%
e 10624096
22.1%
c 5308142
11.0%
i 2196406
 
4.6%
r 2091808
 
4.3%
o 2020260
 
4.2%
l 1542594
 
3.2%
t 1385215
 
2.9%
n 1300651
 
2.7%
s 1002425
 
2.1%
Other values (47) 9656254
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48106205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10978354
22.8%
e 10624096
22.1%
c 5308142
11.0%
i 2196406
 
4.6%
r 2091808
 
4.3%
o 2020260
 
4.2%
l 1542594
 
3.2%
t 1385215
 
2.9%
n 1300651
 
2.7%
s 1002425
 
2.1%
Other values (47) 9656254
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48106205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10978354
22.8%
e 10624096
22.1%
c 5308142
11.0%
i 2196406
 
4.6%
r 2091808
 
4.3%
o 2020260
 
4.2%
l 1542594
 
3.2%
t 1385215
 
2.9%
n 1300651
 
2.7%
s 1002425
 
2.1%
Other values (47) 9656254
20.1%

genus
Text

Distinct19533
Distinct (%)0.4%
Missing13790
Missing (%)0.3%
Memory size34.5 MiB
2025-03-04T14:38:01.984951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length21
Mean length8.779886629
Min length2

Characters and Unicode

Total characters39525917
Distinct characters59
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

Unique2723 ?
Unique (%)0.1%

Sample

1st rowLithothamnion
2nd rowAmicia
3rd rowTripogandra
4th rowConnarus
5th rowTrichoneura
ValueCountFrequency (%)
carex 58129
 
1.3%
indet 34179
 
0.8%
poa 30191
 
0.7%
cyperus 27842
 
0.6%
cladonia 26929
 
0.6%
paspalum 26142
 
0.6%
solanum 24852
 
0.6%
miconia 24577
 
0.5%
eragrostis 23701
 
0.5%
asplenium 20032
 
0.4%
Other values (19524) 4220155
93.4%
2025-03-04T14:38:02.195752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4832994
 
12.2%
i 3638319
 
9.2%
o 2765978
 
7.0%
e 2764006
 
7.0%
r 2582882
 
6.5%
l 2161942
 
5.5%
n 2070715
 
5.2%
s 2058198
 
5.2%
u 1993081
 
5.0%
t 1685888
 
4.3%
Other values (49) 12971914
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39525917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4832994
 
12.2%
i 3638319
 
9.2%
o 2765978
 
7.0%
e 2764006
 
7.0%
r 2582882
 
6.5%
l 2161942
 
5.5%
n 2070715
 
5.2%
s 2058198
 
5.2%
u 1993081
 
5.0%
t 1685888
 
4.3%
Other values (49) 12971914
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39525917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4832994
 
12.2%
i 3638319
 
9.2%
o 2765978
 
7.0%
e 2764006
 
7.0%
r 2582882
 
6.5%
l 2161942
 
5.5%
n 2070715
 
5.2%
s 2058198
 
5.2%
u 1993081
 
5.0%
t 1685888
 
4.3%
Other values (49) 12971914
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39525917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4832994
 
12.2%
i 3638319
 
9.2%
o 2765978
 
7.0%
e 2764006
 
7.0%
r 2582882
 
6.5%
l 2161942
 
5.5%
n 2070715
 
5.2%
s 2058198
 
5.2%
u 1993081
 
5.0%
t 1685888
 
4.3%
Other values (49) 12971914
32.8%

subgenus
Text

Missing 

Distinct14
Distinct (%)15.7%
Missing4515572
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:02.246341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length12.14606742
Min length6

Characters and Unicode

Total characters1081
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

Unique5 ?
Unique (%)5.6%

Sample

1st rowChoanopsis
2nd rowLeptostemonum
3rd rowLeptostemonum
4th rowPseudopoa
5th rowLeptostemonum
ValueCountFrequency (%)
leptostemonum 41
45.6%
meniscium 13
 
14.4%
goniophlebiopteris 10
 
11.1%
pseudopoa 6
 
6.7%
choanopsis 5
 
5.6%
penzigia 3
 
3.3%
arenariae 2
 
2.2%
trichochloa 2
 
2.2%
pseudolysimachium 2
 
2.2%
limnochloa 1
 
1.1%
Other values (5) 5
 
5.6%
2025-03-04T14:38:02.337515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%
Distinct75699
Distinct (%)1.7%
Missing19440
Missing (%)0.4%
Memory size34.5 MiB
2025-03-04T14:38:02.486843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length8.783425904
Min length2

Characters and Unicode

Total characters39492224
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

Unique20935 ?
Unique (%)0.5%

Sample

1st rowcalcareum
2nd rowglandulosa
3rd rowglandulosa
4th rowsteyermarkii
5th rowgrandiglumis
ValueCountFrequency (%)
sp 270345
 
6.0%
canadensis 11707
 
0.3%
guianensis 11466
 
0.3%
americana 11279
 
0.3%
latifolia 11149
 
0.2%
repens 10154
 
0.2%
parviflora 10009
 
0.2%
occidentalis 9667
 
0.2%
gracilis 9142
 
0.2%
indica 9037
 
0.2%
Other values (75609) 4133864
91.9%
2025-03-04T14:38:02.717677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5237374
13.3%
i 4468913
11.3%
s 3076318
 
7.8%
e 2756650
 
7.0%
r 2532177
 
6.4%
l 2515617
 
6.4%
n 2415168
 
6.1%
u 2268906
 
5.7%
o 2256284
 
5.7%
t 2041234
 
5.2%
Other values (45) 9923583
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39492224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5237374
13.3%
i 4468913
11.3%
s 3076318
 
7.8%
e 2756650
 
7.0%
r 2532177
 
6.4%
l 2515617
 
6.4%
n 2415168
 
6.1%
u 2268906
 
5.7%
o 2256284
 
5.7%
t 2041234
 
5.2%
Other values (45) 9923583
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39492224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5237374
13.3%
i 4468913
11.3%
s 3076318
 
7.8%
e 2756650
 
7.0%
r 2532177
 
6.4%
l 2515617
 
6.4%
n 2415168
 
6.1%
u 2268906
 
5.7%
o 2256284
 
5.7%
t 2041234
 
5.2%
Other values (45) 9923583
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39492224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5237374
13.3%
i 4468913
11.3%
s 3076318
 
7.8%
e 2756650
 
7.0%
r 2532177
 
6.4%
l 2515617
 
6.4%
n 2415168
 
6.1%
u 2268906
 
5.7%
o 2256284
 
5.7%
t 2041234
 
5.2%
Other values (45) 9923583
25.1%

infraspecificEpithet
Text

Missing 

Distinct13508
Distinct (%)4.2%
Missing4196068
Missing (%)92.9%
Memory size34.5 MiB
2025-03-04T14:38:02.851448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length29
Mean length9.193180076
Min length1

Characters and Unicode

Total characters2938076
Distinct characters48
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

Unique4773 ?
Unique (%)1.5%

Sample

1st rowoxyphylla
2nd rowsubalpinum
3rd rowpurpurescens
4th rowpubescens
5th rowhirsuta
ValueCountFrequency (%)
acuminatum 4368
 
1.4%
pubescens 1879
 
0.6%
secunda 1646
 
0.5%
dichotomum 1521
 
0.5%
americana 1487
 
0.5%
gracilis 1466
 
0.5%
angustifolia 1339
 
0.4%
typica 1218
 
0.4%
occidentalis 1214
 
0.4%
glauca 1198
 
0.4%
Other values (13459) 302708
94.6%
2025-03-04T14:38:03.054237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 401235
13.7%
i 336149
11.4%
s 216184
 
7.4%
e 204762
 
7.0%
l 195545
 
6.7%
n 184185
 
6.3%
r 181755
 
6.2%
u 176685
 
6.0%
o 167405
 
5.7%
t 150042
 
5.1%
Other values (38) 724129
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2938076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 401235
13.7%
i 336149
11.4%
s 216184
 
7.4%
e 204762
 
7.0%
l 195545
 
6.7%
n 184185
 
6.3%
r 181755
 
6.2%
u 176685
 
6.0%
o 167405
 
5.7%
t 150042
 
5.1%
Other values (38) 724129
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2938076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 401235
13.7%
i 336149
11.4%
s 216184
 
7.4%
e 204762
 
7.0%
l 195545
 
6.7%
n 184185
 
6.3%
r 181755
 
6.2%
u 176685
 
6.0%
o 167405
 
5.7%
t 150042
 
5.1%
Other values (38) 724129
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2938076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 401235
13.7%
i 336149
11.4%
s 216184
 
7.4%
e 204762
 
7.0%
l 195545
 
6.7%
n 184185
 
6.3%
r 181755
 
6.2%
u 176685
 
6.0%
o 167405
 
5.7%
t 150042
 
5.1%
Other values (38) 724129
24.6%

cultivarEpithet
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing4515659
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:03.103044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length14.5
Mean length14.5
Min length13

Characters and Unicode

Total characters29
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 (%)100.0%

Sample

1st rowMelica torreyana
2nd rowDiplazium sp.
ValueCountFrequency (%)
melica 1
25.0%
torreyana 1
25.0%
diplazium 1
25.0%
sp 1
25.0%
2025-03-04T14:38:03.190876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4
13.8%
i 3
 
10.3%
r 2
 
6.9%
l 2
 
6.9%
2
 
6.9%
e 2
 
6.9%
p 2
 
6.9%
D 1
 
3.4%
s 1
 
3.4%
m 1
 
3.4%
Other values (9) 9
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4
13.8%
i 3
 
10.3%
r 2
 
6.9%
l 2
 
6.9%
2
 
6.9%
e 2
 
6.9%
p 2
 
6.9%
D 1
 
3.4%
s 1
 
3.4%
m 1
 
3.4%
Other values (9) 9
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4
13.8%
i 3
 
10.3%
r 2
 
6.9%
l 2
 
6.9%
2
 
6.9%
e 2
 
6.9%
p 2
 
6.9%
D 1
 
3.4%
s 1
 
3.4%
m 1
 
3.4%
Other values (9) 9
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4
13.8%
i 3
 
10.3%
r 2
 
6.9%
l 2
 
6.9%
2
 
6.9%
e 2
 
6.9%
p 2
 
6.9%
D 1
 
3.4%
s 1
 
3.4%
m 1
 
3.4%
Other values (9) 9
31.0%

taxonRank
Text

Missing 

Distinct28
Distinct (%)< 0.1%
Missing4196350
Missing (%)92.9%
Memory size34.5 MiB
2025-03-04T14:38:03.224272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.875528873
Min length2

Characters and Unicode

Total characters2514743
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowvariety
2nd rowVariety
3rd rowvariety
4th rowsubspecies
5th rowVariety
ValueCountFrequency (%)
variety 207363
64.9%
subspecies 101047
31.6%
forma 8236
 
2.6%
var 2270
 
0.7%
form 85
 
< 0.1%
subvariety 81
 
< 0.1%
aff 73
 
< 0.1%
nothosubsp 57
 
< 0.1%
agg 18
 
< 0.1%
fo 18
 
< 0.1%
Other values (15) 63
 
< 0.1%
2025-03-04T14:38:03.309093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 409561
16.3%
i 308499
12.3%
s 303355
12.1%
a 218079
8.7%
r 218075
8.7%
t 207516
8.3%
y 207450
8.2%
v 181926
7.2%
u 101201
 
4.0%
b 101194
 
4.0%
Other values (16) 257887
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2514743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 409561
16.3%
i 308499
12.3%
s 303355
12.1%
a 218079
8.7%
r 218075
8.7%
t 207516
8.3%
y 207450
8.2%
v 181926
7.2%
u 101201
 
4.0%
b 101194
 
4.0%
Other values (16) 257887
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2514743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 409561
16.3%
i 308499
12.3%
s 303355
12.1%
a 218079
8.7%
r 218075
8.7%
t 207516
8.3%
y 207450
8.2%
v 181926
7.2%
u 101201
 
4.0%
b 101194
 
4.0%
Other values (16) 257887
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2514743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 409561
16.3%
i 308499
12.3%
s 303355
12.1%
a 218079
8.7%
r 218075
8.7%
t 207516
8.3%
y 207450
8.2%
v 181926
7.2%
u 101201
 
4.0%
b 101194
 
4.0%
Other values (16) 257887
10.3%
Distinct61211
Distinct (%)1.5%
Missing491289
Missing (%)10.9%
Memory size34.5 MiB
2025-03-04T14:38:03.440638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length63
Mean length11.67467396
Min length2

Characters and Unicode

Total characters46983231
Distinct characters115
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

Unique12830 ?
Unique (%)0.3%

Sample

1st rowKunth
2nd row(Seub.) Rohweder
3rd rowPrance
4th row(Nees) Ekman
5th row(Britton ex Rusby) Wiehler
ValueCountFrequency (%)
l 655549
 
7.4%
528030
 
6.0%
ex 293982
 
3.3%
a 184606
 
2.1%
dc 137922
 
1.6%
kunth 108763
 
1.2%
gray 104768
 
1.2%
benth 100384
 
1.1%
sw 88430
 
1.0%
hook 85187
 
1.0%
Other values (10671) 6515117
74.0%
2025-03-04T14:38:03.736204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 5755821
 
12.3%
4778366
 
10.2%
e 2841039
 
6.0%
r 2154959
 
4.6%
a 1898912
 
4.0%
l 1877752
 
4.0%
n 1788431
 
3.8%
( 1676769
 
3.6%
) 1676769
 
3.6%
o 1598443
 
3.4%
Other values (105) 20935970
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46983231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 5755821
 
12.3%
4778366
 
10.2%
e 2841039
 
6.0%
r 2154959
 
4.6%
a 1898912
 
4.0%
l 1877752
 
4.0%
n 1788431
 
3.8%
( 1676769
 
3.6%
) 1676769
 
3.6%
o 1598443
 
3.4%
Other values (105) 20935970
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46983231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 5755821
 
12.3%
4778366
 
10.2%
e 2841039
 
6.0%
r 2154959
 
4.6%
a 1898912
 
4.0%
l 1877752
 
4.0%
n 1788431
 
3.8%
( 1676769
 
3.6%
) 1676769
 
3.6%
o 1598443
 
3.4%
Other values (105) 20935970
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46983231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 5755821
 
12.3%
4778366
 
10.2%
e 2841039
 
6.0%
r 2154959
 
4.6%
a 1898912
 
4.0%
l 1877752
 
4.0%
n 1788431
 
3.8%
( 1676769
 
3.6%
) 1676769
 
3.6%
o 1598443
 
3.4%
Other values (105) 20935970
44.6%

vernacularName
Text

Missing 

Distinct2
Distinct (%)66.7%
Missing4515658
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:03.770947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.666666667
Min length7

Characters and Unicode

Total characters23
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowHolotype
2nd rowIsotype
3rd rowHolotype
ValueCountFrequency (%)
holotype 2
66.7%
isotype 1
33.3%
2025-03-04T14:38:03.853061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5
21.7%
t 3
13.0%
y 3
13.0%
p 3
13.0%
e 3
13.0%
H 2
 
8.7%
l 2
 
8.7%
I 1
 
4.3%
s 1
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5
21.7%
t 3
13.0%
y 3
13.0%
p 3
13.0%
e 3
13.0%
H 2
 
8.7%
l 2
 
8.7%
I 1
 
4.3%
s 1
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5
21.7%
t 3
13.0%
y 3
13.0%
p 3
13.0%
e 3
13.0%
H 2
 
8.7%
l 2
 
8.7%
I 1
 
4.3%
s 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5
21.7%
t 3
13.0%
y 3
13.0%
p 3
13.0%
e 3
13.0%
H 2
 
8.7%
l 2
 
8.7%
I 1
 
4.3%
s 1
 
4.3%

nomenclaturalCode
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4515660
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:03.882207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowSkog, Laurence E.
ValueCountFrequency (%)
skog 1
33.3%
laurence 1
33.3%
e 1
33.3%
2025-03-04T14:38:03.960290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
 
11.8%
e 2
 
11.8%
S 1
 
5.9%
k 1
 
5.9%
o 1
 
5.9%
g 1
 
5.9%
, 1
 
5.9%
L 1
 
5.9%
a 1
 
5.9%
u 1
 
5.9%
Other values (5) 5
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2
 
11.8%
e 2
 
11.8%
S 1
 
5.9%
k 1
 
5.9%
o 1
 
5.9%
g 1
 
5.9%
, 1
 
5.9%
L 1
 
5.9%
a 1
 
5.9%
u 1
 
5.9%
Other values (5) 5
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2
 
11.8%
e 2
 
11.8%
S 1
 
5.9%
k 1
 
5.9%
o 1
 
5.9%
g 1
 
5.9%
, 1
 
5.9%
L 1
 
5.9%
a 1
 
5.9%
u 1
 
5.9%
Other values (5) 5
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2
 
11.8%
e 2
 
11.8%
S 1
 
5.9%
k 1
 
5.9%
o 1
 
5.9%
g 1
 
5.9%
, 1
 
5.9%
L 1
 
5.9%
a 1
 
5.9%
u 1
 
5.9%
Other values (5) 5
29.4%

nomenclaturalStatus
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing4515659
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:03.994342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length49.5
Mean length49.5
Min length48

Characters and Unicode

Total characters99
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 (%)100.0%

Sample

1st rowPlantae, Monocotyledonae, Poales, Poaceae, Pooideae
2nd rowPlantae, Pteridophyte, Polypodiales, Athyriaceae
ValueCountFrequency (%)
plantae 2
22.2%
monocotyledonae 1
11.1%
poales 1
11.1%
poaceae 1
11.1%
pooideae 1
11.1%
pteridophyte 1
11.1%
polypodiales 1
11.1%
athyriaceae 1
11.1%
2025-03-04T14:38:04.082813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14
14.1%
a 12
12.1%
o 11
11.1%
7
 
7.1%
, 7
 
7.1%
P 7
 
7.1%
t 6
 
6.1%
l 6
 
6.1%
n 4
 
4.0%
y 4
 
4.0%
Other values (9) 21
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14
14.1%
a 12
12.1%
o 11
11.1%
7
 
7.1%
, 7
 
7.1%
P 7
 
7.1%
t 6
 
6.1%
l 6
 
6.1%
n 4
 
4.0%
y 4
 
4.0%
Other values (9) 21
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14
14.1%
a 12
12.1%
o 11
11.1%
7
 
7.1%
, 7
 
7.1%
P 7
 
7.1%
t 6
 
6.1%
l 6
 
6.1%
n 4
 
4.0%
y 4
 
4.0%
Other values (9) 21
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14
14.1%
a 12
12.1%
o 11
11.1%
7
 
7.1%
, 7
 
7.1%
P 7
 
7.1%
t 6
 
6.1%
l 6
 
6.1%
n 4
 
4.0%
y 4
 
4.0%
Other values (9) 21
21.2%

taxonRemarks
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing4515659
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-04T14:38:04.114278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlantae
2nd rowPlantae
ValueCountFrequency (%)
plantae 2
100.0%
2025-03-04T14:38:04.191202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4
28.6%
P 2
14.3%
l 2
14.3%
n 2
14.3%
t 2
14.3%
e 2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4
28.6%
P 2
14.3%
l 2
14.3%
n 2
14.3%
t 2
14.3%
e 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4
28.6%
P 2
14.3%
l 2
14.3%
n 2
14.3%
t 2
14.3%
e 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4
28.6%
P 2
14.3%
l 2
14.3%
n 2
14.3%
t 2
14.3%
e 2
14.3%