Overview

Brought to you by YData

Dataset statistics

Number of variables91
Number of observations547708
Missing cells28403890
Missing cells (%)57.0%
Total size in memory380.3 MiB
Average record size in memory728.0 B

Variable types

Text91

Dataset

DescriptionCarnegie Museum of Natural History Herbarium 0000014-250226211518916
URLhttps://doi.org/10.15468/dl.59avmn

Alerts

rightsHolder has constant value "Carnegie Museum of Natural History" Constant
collectionID has constant value "a3883acd-bc7d-40f2-a6e4-372a3435e5d1" Constant
institutionCode has constant value "CM" Constant
basisOfRecord has constant value "PreservedSpecimen" Constant
establishmentMeans has constant value "cultivated" Constant
disposition has constant value "27-Jun-03" Constant
samplingProtocol has constant value "Uintah" Constant
sampleSizeValue has constant value "152" Constant
samplingEffort has constant value "~500 ft" Constant
higherGeographyID has constant value "Bonnie L. Isaac" Constant
islandGroup has constant value "Translated from given coordinates" Constant
island has constant value "40.130056" Constant
countryCode has constant value "WGS84" Constant
verbatimDepth has constant value "Bonnie L. Isaac" Constant
maximumDistanceAboveSurfaceInMeters has constant value "coordinates given" Constant
verbatimCoordinateSystem has constant value "4235" Constant
earliestEraOrLowestErathem has constant value "B.L. Isaac 2023" Constant
earliestPeriodOrLowestSystem has constant value "Plantae|Spermatophyta|Tracheophyta|Magnoliophyta|Eudicots|Core Eudicots|Fabids|Rosids|Rosales|Rosaceae" Constant
latestPeriodOrHighestSystem has constant value "Plantae" Constant
earliestEpochOrLowestSeries has constant value "Magnoliophyta" Constant
lowestBiostratigraphicZone has constant value "Rosaceae" Constant
formation has constant value "Rubus" Constant
bed has constant value "Cinna arundinacea" Constant
identificationVerificationStatus has constant value "Magnoliophyta" Constant
acceptedNameUsageID has constant value "Poaceae" Constant
namePublishedInID has constant value "Cinna" Constant
parentNameUsage has constant value "arundinacea" Constant
namePublishedIn has constant value "Species" Constant
informationWithheld has 536378 (97.9%) missing values Missing
dynamicProperties has 547706 (> 99.9%) missing values Missing
recordNumber has 20723 (3.8%) missing values Missing
recordedBy has 8448 (1.5%) missing values Missing
reproductiveCondition has 110994 (20.3%) missing values Missing
establishmentMeans has 547707 (> 99.9%) missing values Missing
georeferenceVerificationStatus has 449157 (82.0%) missing values Missing
preparations has 546820 (99.8%) missing values Missing
disposition has 547707 (> 99.9%) missing values Missing
associatedOccurrences has 503313 (91.9%) missing values Missing
associatedTaxa has 503313 (91.9%) missing values Missing
otherCatalogNumbers has 547697 (> 99.9%) missing values Missing
occurrenceRemarks has 547601 (> 99.9%) missing values Missing
eventDate has 27372 (5.0%) missing values Missing
startDayOfYear has 83521 (15.2%) missing values Missing
endDayOfYear has 547704 (> 99.9%) missing values Missing
year has 16300 (3.0%) missing values Missing
month has 23216 (4.2%) missing values Missing
day has 25750 (4.7%) missing values Missing
verbatimEventDate has 76931 (14.0%) missing values Missing
habitat has 238673 (43.6%) missing values Missing
samplingProtocol has 547707 (> 99.9%) missing values Missing
sampleSizeValue has 547707 (> 99.9%) missing values Missing
samplingEffort has 547707 (> 99.9%) missing values Missing
higherGeographyID has 547707 (> 99.9%) missing values Missing
islandGroup has 547707 (> 99.9%) missing values Missing
island has 547707 (> 99.9%) missing values Missing
countryCode has 547707 (> 99.9%) missing values Missing
stateProvince has 47031 (8.6%) missing values Missing
county has 135709 (24.8%) missing values Missing
locality has 18209 (3.3%) missing values Missing
minimumElevationInMeters has 399055 (72.9%) missing values Missing
maximumElevationInMeters has 532633 (97.2%) missing values Missing
verbatimElevation has 396776 (72.4%) missing values Missing
verbatimDepth has 547707 (> 99.9%) missing values Missing
maximumDistanceAboveSurfaceInMeters has 547707 (> 99.9%) missing values Missing
locationRemarks has 547560 (> 99.9%) missing values Missing
decimalLatitude has 232306 (42.4%) missing values Missing
decimalLongitude has 232306 (42.4%) missing values Missing
geodeticDatum has 274064 (50.0%) missing values Missing
coordinateUncertaintyInMeters has 277914 (50.7%) missing values Missing
verbatimCoordinates has 81905 (15.0%) missing values Missing
verbatimCoordinateSystem has 547707 (> 99.9%) missing values Missing
georeferencedBy has 310094 (56.6%) missing values Missing
georeferenceProtocol has 544682 (99.4%) missing values Missing
georeferenceSources has 336656 (61.5%) missing values Missing
georeferenceRemarks has 498455 (91.0%) missing values Missing
earliestEraOrLowestErathem has 547707 (> 99.9%) missing values Missing
earliestPeriodOrLowestSystem has 547707 (> 99.9%) missing values Missing
latestPeriodOrHighestSystem has 547707 (> 99.9%) missing values Missing
earliestEpochOrLowestSeries has 547707 (> 99.9%) missing values Missing
earliestAgeOrLowestStage has 547706 (> 99.9%) missing values Missing
lowestBiostratigraphicZone has 547707 (> 99.9%) missing values Missing
formation has 547707 (> 99.9%) missing values Missing
bed has 547707 (> 99.9%) missing values Missing
identificationQualifier has 544285 (99.4%) missing values Missing
typeStatus has 544285 (99.4%) missing values Missing
identifiedBy has 279934 (51.1%) missing values Missing
dateIdentified has 547703 (> 99.9%) missing values Missing
identificationReferences has 547703 (> 99.9%) missing values Missing
identificationVerificationStatus has 547707 (> 99.9%) missing values Missing
identificationRemarks has 384410 (70.2%) missing values Missing
taxonID has 9329 (1.7%) missing values Missing
acceptedNameUsageID has 547707 (> 99.9%) missing values Missing
namePublishedInID has 547707 (> 99.9%) missing values Missing
parentNameUsage has 547707 (> 99.9%) missing values Missing
namePublishedIn has 547707 (> 99.9%) missing values Missing
higherClassification has 8875 (1.6%) missing values Missing
phylum has 8876 (1.6%) missing values Missing
class has 512299 (93.5%) missing values Missing
order has 8877 (1.6%) missing values Missing
family has 8850 (1.6%) missing values Missing
genus has 10772 (2.0%) missing values Missing
specificEpithet has 20227 (3.7%) missing values Missing
infraspecificEpithet has 505808 (92.3%) missing values Missing
taxonRank has 9330 (1.7%) missing values Missing
verbatimTaxonRank has 505808 (92.3%) missing values Missing
scientificNameAuthorship has 24606 (4.5%) missing values Missing
gbifID has unique values Unique
references has unique values Unique
occurrenceID has unique values Unique
catalogNumber has unique values Unique

Reproduction

Analysis started2025-03-04 19:24:27.715253
Analysis finished2025-03-04 19:24:52.183039
Duration24.47 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct547708
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:52.821677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5477080
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

Unique547708 ?
Unique (%)100.0%

Sample

1st row2859232301
2nd row2858977301
3rd row2858976301
4th row2859255301
5th row2859232303
ValueCountFrequency (%)
2859232301 1
 
< 0.1%
2858975307 1
 
< 0.1%
2859232303 1
 
< 0.1%
2858975302 1
 
< 0.1%
2858977302 1
 
< 0.1%
2859255303 1
 
< 0.1%
2858976303 1
 
< 0.1%
2858975304 1
 
< 0.1%
2859232305 1
 
< 0.1%
2858977305 1
 
< 0.1%
Other values (547698) 547698
> 99.9%
2025-03-04T14:24:53.429247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 902936
16.5%
8 846493
15.5%
9 819960
15.0%
5 814885
14.9%
3 394191
7.2%
0 383563
7.0%
4 380214
6.9%
1 376949
6.9%
7 281948
 
5.1%
6 275941
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5477080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 902936
16.5%
8 846493
15.5%
9 819960
15.0%
5 814885
14.9%
3 394191
7.2%
0 383563
7.0%
4 380214
6.9%
1 376949
6.9%
7 281948
 
5.1%
6 275941
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5477080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 902936
16.5%
8 846493
15.5%
9 819960
15.0%
5 814885
14.9%
3 394191
7.2%
0 383563
7.0%
4 380214
6.9%
1 376949
6.9%
7 281948
 
5.1%
6 275941
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5477080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 902936
16.5%
8 846493
15.5%
9 819960
15.0%
5 814885
14.9%
3 394191
7.2%
0 383563
7.0%
4 380214
6.9%
1 376949
6.9%
7 281948
 
5.1%
6 275941
 
5.0%
Distinct3237
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:53.483066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters10406452
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

Unique367 ?
Unique (%)0.1%

Sample

1st row2009-12-21 00:00:00
2nd row2009-12-21 00:00:00
3rd row2009-12-21 00:00:00
4th row2009-12-21 00:00:00
5th row2009-12-21 00:00:00
ValueCountFrequency (%)
00:00:00 489148
44.7%
2024-07-02 54841
 
5.0%
12:19:37 54481
 
5.0%
2006-11-30 14769
 
1.3%
2007-06-08 10068
 
0.9%
2006-09-28 9696
 
0.9%
2024-05-24 8651
 
0.8%
2007-02-23 8388
 
0.8%
2007-04-20 8205
 
0.7%
2004-08-10 6347
 
0.6%
Other values (3234) 430822
39.3%
2025-03-04T14:24:53.562988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4434974
42.6%
- 1095416
 
10.5%
: 1095416
 
10.5%
2 1077116
 
10.4%
1 720273
 
6.9%
547708
 
5.3%
7 268425
 
2.6%
9 255901
 
2.5%
3 250729
 
2.4%
4 233080
 
2.2%
Other values (3) 427414
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10406452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4434974
42.6%
- 1095416
 
10.5%
: 1095416
 
10.5%
2 1077116
 
10.4%
1 720273
 
6.9%
547708
 
5.3%
7 268425
 
2.6%
9 255901
 
2.5%
3 250729
 
2.4%
4 233080
 
2.2%
Other values (3) 427414
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10406452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4434974
42.6%
- 1095416
 
10.5%
: 1095416
 
10.5%
2 1077116
 
10.4%
1 720273
 
6.9%
547708
 
5.3%
7 268425
 
2.6%
9 255901
 
2.5%
3 250729
 
2.4%
4 233080
 
2.2%
Other values (3) 427414
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10406452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4434974
42.6%
- 1095416
 
10.5%
: 1095416
 
10.5%
2 1077116
 
10.4%
1 720273
 
6.9%
547708
 
5.3%
7 268425
 
2.6%
9 255901
 
2.5%
3 250729
 
2.4%
4 233080
 
2.2%
Other values (3) 427414
 
4.1%

references
Text

Unique 

Distinct547708
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:53.809656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length86
Median length86
Mean length86
Min length86

Characters and Unicode

Total characters47102888
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

Unique547708 ?
Unique (%)100.0%

Sample

1st rowhttps://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11612976
2nd rowhttps://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11612987
3rd rowhttps://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11612990
4th rowhttps://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11612998
5th rowhttps://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613001
ValueCountFrequency (%)
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11612976 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613108 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613001 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613009 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613012 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613023 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613034 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613045 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613056 1
 
< 0.1%
https://midatlanticherbaria.org/portal/collections/individual/index.php?occid=11613067 1
 
< 0.1%
Other values (547698) 547698
> 99.9%
2025-03-04T14:24:54.112778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4929372
 
10.5%
t 3286248
 
7.0%
/ 3286248
 
7.0%
a 3286248
 
7.0%
o 2738540
 
5.8%
c 2738540
 
5.8%
d 2738540
 
5.8%
l 2738540
 
5.8%
p 2190832
 
4.7%
r 2190832
 
4.7%
Other values (24) 16978948
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47102888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4929372
 
10.5%
t 3286248
 
7.0%
/ 3286248
 
7.0%
a 3286248
 
7.0%
o 2738540
 
5.8%
c 2738540
 
5.8%
d 2738540
 
5.8%
l 2738540
 
5.8%
p 2190832
 
4.7%
r 2190832
 
4.7%
Other values (24) 16978948
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47102888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4929372
 
10.5%
t 3286248
 
7.0%
/ 3286248
 
7.0%
a 3286248
 
7.0%
o 2738540
 
5.8%
c 2738540
 
5.8%
d 2738540
 
5.8%
l 2738540
 
5.8%
p 2190832
 
4.7%
r 2190832
 
4.7%
Other values (24) 16978948
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47102888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4929372
 
10.5%
t 3286248
 
7.0%
/ 3286248
 
7.0%
a 3286248
 
7.0%
o 2738540
 
5.8%
c 2738540
 
5.8%
d 2738540
 
5.8%
l 2738540
 
5.8%
p 2190832
 
4.7%
r 2190832
 
4.7%
Other values (24) 16978948
36.0%

rightsHolder
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:54.157504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length34
Mean length34
Min length34

Characters and Unicode

Total characters18622072
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarnegie Museum of Natural History
2nd rowCarnegie Museum of Natural History
3rd rowCarnegie Museum of Natural History
4th rowCarnegie Museum of Natural History
5th rowCarnegie Museum of Natural History
ValueCountFrequency (%)
carnegie 547708
20.0%
museum 547708
20.0%
of 547708
20.0%
natural 547708
20.0%
history 547708
20.0%
2025-03-04T14:24:54.243903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2190832
11.8%
u 1643124
 
8.8%
r 1643124
 
8.8%
e 1643124
 
8.8%
a 1643124
 
8.8%
i 1095416
 
5.9%
t 1095416
 
5.9%
s 1095416
 
5.9%
o 1095416
 
5.9%
f 547708
 
2.9%
Other values (9) 4929372
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18622072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2190832
11.8%
u 1643124
 
8.8%
r 1643124
 
8.8%
e 1643124
 
8.8%
a 1643124
 
8.8%
i 1095416
 
5.9%
t 1095416
 
5.9%
s 1095416
 
5.9%
o 1095416
 
5.9%
f 547708
 
2.9%
Other values (9) 4929372
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18622072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2190832
11.8%
u 1643124
 
8.8%
r 1643124
 
8.8%
e 1643124
 
8.8%
a 1643124
 
8.8%
i 1095416
 
5.9%
t 1095416
 
5.9%
s 1095416
 
5.9%
o 1095416
 
5.9%
f 547708
 
2.9%
Other values (9) 4929372
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18622072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2190832
11.8%
u 1643124
 
8.8%
r 1643124
 
8.8%
e 1643124
 
8.8%
a 1643124
 
8.8%
i 1095416
 
5.9%
t 1095416
 
5.9%
s 1095416
 
5.9%
o 1095416
 
5.9%
f 547708
 
2.9%
Other values (9) 4929372
26.5%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:54.272402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters19717488
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowa3883acd-bc7d-40f2-a6e4-372a3435e5d1
2nd rowa3883acd-bc7d-40f2-a6e4-372a3435e5d1
3rd rowa3883acd-bc7d-40f2-a6e4-372a3435e5d1
4th rowa3883acd-bc7d-40f2-a6e4-372a3435e5d1
5th rowa3883acd-bc7d-40f2-a6e4-372a3435e5d1
ValueCountFrequency (%)
a3883acd-bc7d-40f2-a6e4-372a3435e5d1 547708
100.0%
2025-03-04T14:24:54.358319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2738540
13.9%
a 2190832
11.1%
- 2190832
11.1%
d 1643124
8.3%
4 1643124
8.3%
8 1095416
 
5.6%
c 1095416
 
5.6%
7 1095416
 
5.6%
2 1095416
 
5.6%
e 1095416
 
5.6%
Other values (6) 3833956
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19717488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2738540
13.9%
a 2190832
11.1%
- 2190832
11.1%
d 1643124
8.3%
4 1643124
8.3%
8 1095416
 
5.6%
c 1095416
 
5.6%
7 1095416
 
5.6%
2 1095416
 
5.6%
e 1095416
 
5.6%
Other values (6) 3833956
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19717488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2738540
13.9%
a 2190832
11.1%
- 2190832
11.1%
d 1643124
8.3%
4 1643124
8.3%
8 1095416
 
5.6%
c 1095416
 
5.6%
7 1095416
 
5.6%
2 1095416
 
5.6%
e 1095416
 
5.6%
Other values (6) 3833956
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19717488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2738540
13.9%
a 2190832
11.1%
- 2190832
11.1%
d 1643124
8.3%
4 1643124
8.3%
8 1095416
 
5.6%
c 1095416
 
5.6%
7 1095416
 
5.6%
2 1095416
 
5.6%
e 1095416
 
5.6%
Other values (6) 3833956
19.4%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:54.386599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1095416
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 rowCM
2nd rowCM
3rd rowCM
4th rowCM
5th rowCM
ValueCountFrequency (%)
cm 547708
100.0%
2025-03-04T14:24:54.466362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 547708
50.0%
M 547708
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1095416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 547708
50.0%
M 547708
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1095416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 547708
50.0%
M 547708
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1095416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 547708
50.0%
M 547708
50.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:54.496030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3286248
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbotany
2nd rowbotany
3rd rowbotany
4th rowbotany
5th rowbotany
ValueCountFrequency (%)
botany 547708
100.0%
2025-03-04T14:24:54.577970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 547708
16.7%
t 547708
16.7%
a 547708
16.7%
n 547708
16.7%
y 547708
16.7%
b 543643
16.5%
B 4065
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3286248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 547708
16.7%
t 547708
16.7%
a 547708
16.7%
n 547708
16.7%
y 547708
16.7%
b 543643
16.5%
B 4065
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3286248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 547708
16.7%
t 547708
16.7%
a 547708
16.7%
n 547708
16.7%
y 547708
16.7%
b 543643
16.5%
B 4065
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3286248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 547708
16.7%
t 547708
16.7%
a 547708
16.7%
n 547708
16.7%
y 547708
16.7%
b 543643
16.5%
B 4065
 
0.1%

basisOfRecord
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:54.606783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters9311036
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPreservedSpecimen
2nd rowPreservedSpecimen
3rd rowPreservedSpecimen
4th rowPreservedSpecimen
5th rowPreservedSpecimen
ValueCountFrequency (%)
preservedspecimen 547708
100.0%
2025-03-04T14:24:54.687726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2738540
29.4%
r 1095416
 
11.8%
P 547708
 
5.9%
s 547708
 
5.9%
v 547708
 
5.9%
d 547708
 
5.9%
S 547708
 
5.9%
p 547708
 
5.9%
c 547708
 
5.9%
i 547708
 
5.9%
Other values (2) 1095416
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9311036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2738540
29.4%
r 1095416
 
11.8%
P 547708
 
5.9%
s 547708
 
5.9%
v 547708
 
5.9%
d 547708
 
5.9%
S 547708
 
5.9%
p 547708
 
5.9%
c 547708
 
5.9%
i 547708
 
5.9%
Other values (2) 1095416
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9311036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2738540
29.4%
r 1095416
 
11.8%
P 547708
 
5.9%
s 547708
 
5.9%
v 547708
 
5.9%
d 547708
 
5.9%
S 547708
 
5.9%
p 547708
 
5.9%
c 547708
 
5.9%
i 547708
 
5.9%
Other values (2) 1095416
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9311036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2738540
29.4%
r 1095416
 
11.8%
P 547708
 
5.9%
s 547708
 
5.9%
v 547708
 
5.9%
d 547708
 
5.9%
S 547708
 
5.9%
p 547708
 
5.9%
c 547708
 
5.9%
i 547708
 
5.9%
Other values (2) 1095416
 
11.8%

informationWithheld
Text

Missing 

Distinct397
Distinct (%)3.5%
Missing536378
Missing (%)97.9%
Memory size4.2 MiB
2025-03-04T14:24:54.748699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length351
Median length319
Mean length229.4070609
Min length35

Characters and Unicode

Total characters2599182
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

Unique131 ?
Unique (%)1.2%

Sample

1st rowfield values redacted: eventDate, month, day, startDayOfYear, verbatimEventDate, recordNumber, locality, decimalLatitude, decimalLongitude, georeferencedBy, georeferenceProtocol
2nd rowfield values redacted: eventDate, month, day, startDayOfYear, verbatimEventDate, recordNumber, locality, decimalLatitude, decimalLongitude, geodeticDatum, coordinateUncertaintyInMeters, verbatimCoordinates, georeferencedBy, georeferenceSources, georeferenceVerificationStatus, habitat
3rd rowfield values redacted: eventDate, month, day, startDayOfYear, verbatimEventDate, recordNumber, locality, verbatimCoordinates
4th rowfield values redacted: eventDate, month, day, startDayOfYear, verbatimEventDate, recordNumber, locality, decimalLatitude, decimalLongitude, geodeticDatum, coordinateUncertaintyInMeters, verbatimCoordinates, georeferencedBy, georeferenceSources
5th rowfield values redacted: eventDate, month, day, startDayOfYear, verbatimEventDate, recordNumber, locality, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, verbatimCoordinates, georeferencedBy, georeferenceSources, georeferenceVerificationStatus, habitat
ValueCountFrequency (%)
field 11330
 
6.2%
redacted 11330
 
6.2%
values 11330
 
6.2%
locality 11302
 
6.1%
recordnumber 11279
 
6.1%
eventdate 11056
 
6.0%
month 10783
 
5.9%
verbatimeventdate 10051
 
5.5%
day 9931
 
5.4%
startdayofyear 9924
 
5.4%
Other values (14) 75880
41.2%
2025-03-04T14:24:54.875743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 370991
14.3%
t 234837
 
9.0%
a 205312
 
7.9%
r 174295
 
6.7%
172866
 
6.7%
i 142735
 
5.5%
, 138876
 
5.3%
d 125771
 
4.8%
o 120989
 
4.7%
n 115124
 
4.4%
Other values (29) 797386
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2599182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 370991
14.3%
t 234837
 
9.0%
a 205312
 
7.9%
r 174295
 
6.7%
172866
 
6.7%
i 142735
 
5.5%
, 138876
 
5.3%
d 125771
 
4.8%
o 120989
 
4.7%
n 115124
 
4.4%
Other values (29) 797386
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2599182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 370991
14.3%
t 234837
 
9.0%
a 205312
 
7.9%
r 174295
 
6.7%
172866
 
6.7%
i 142735
 
5.5%
, 138876
 
5.3%
d 125771
 
4.8%
o 120989
 
4.7%
n 115124
 
4.4%
Other values (29) 797386
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2599182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 370991
14.3%
t 234837
 
9.0%
a 205312
 
7.9%
r 174295
 
6.7%
172866
 
6.7%
i 142735
 
5.5%
, 138876
 
5.3%
d 125771
 
4.8%
o 120989
 
4.7%
n 115124
 
4.4%
Other values (29) 797386
30.7%

dynamicProperties
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing547706
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:24:54.918209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length140
Median length140
Mean length140
Min length140

Characters and Unicode

Total characters280
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowexsiccatae: {"exsTitle":"Southern Appalachian Botanical Club 04th Distribution of Southeastern Plants","exsRange":"300-?","exsNumber":"307"}
2nd rowexsiccatae: {"exsTitle":"Southern Appalachian Botanical Club 02nd Distribution of Southeastern Plants","exsRange":"100-?","exsNumber":"127"}
ValueCountFrequency (%)
exsiccatae 2
10.0%
exstitle":"southern 2
10.0%
appalachian 2
10.0%
botanical 2
10.0%
club 2
10.0%
distribution 2
10.0%
of 2
10.0%
southeastern 2
10.0%
04th 1
5.0%
plants","exsrange":"300-?","exsnumber":"307 1
5.0%
Other values (2) 2
10.0%
2025-03-04T14:24:55.015114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 24
 
8.6%
e 22
 
7.9%
a 20
 
7.1%
t 19
 
6.8%
18
 
6.4%
n 15
 
5.4%
s 14
 
5.0%
i 14
 
5.0%
o 10
 
3.6%
l 10
 
3.6%
Other values (32) 114
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 24
 
8.6%
e 22
 
7.9%
a 20
 
7.1%
t 19
 
6.8%
18
 
6.4%
n 15
 
5.4%
s 14
 
5.0%
i 14
 
5.0%
o 10
 
3.6%
l 10
 
3.6%
Other values (32) 114
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 24
 
8.6%
e 22
 
7.9%
a 20
 
7.1%
t 19
 
6.8%
18
 
6.4%
n 15
 
5.4%
s 14
 
5.0%
i 14
 
5.0%
o 10
 
3.6%
l 10
 
3.6%
Other values (32) 114
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 24
 
8.6%
e 22
 
7.9%
a 20
 
7.1%
t 19
 
6.8%
18
 
6.4%
n 15
 
5.4%
s 14
 
5.0%
i 14
 
5.0%
o 10
 
3.6%
l 10
 
3.6%
Other values (32) 114
40.7%

occurrenceID
Text

Unique 

Distinct547708
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:55.254606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length36
Mean length36.00000365
Min length36

Characters and Unicode

Total characters19717490
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

Unique547708 ?
Unique (%)100.0%

Sample

1st rowA090A2B5-8057-4114-B061-62E31CC04283
2nd row4FB26CA8-5538-4A66-998A-80425D1D8FEF
3rd rowD38D4A91-5BBD-45E5-9CAE-959A0A8F80D2
4th row4137F793-0AEC-4E2A-8EAF-6803932D775F
5th rowC8410A65-98B4-4D6E-8C3D-71B6C4BB84C7
ValueCountFrequency (%)
a090a2b5-8057-4114-b061-62e31cc04283 1
 
< 0.1%
279995f0-8ece-408d-af22-3124d466af96 1
 
< 0.1%
c8410a65-98b4-4d6e-8c3d-71b6c4bb84c7 1
 
< 0.1%
105fdf6f-05f4-4cd2-8af0-e02d1215a3fd 1
 
< 0.1%
b98620bd-2079-46bf-a550-6a5e83ae38d8 1
 
< 0.1%
7f9f5f18-3267-4126-b407-afd7fe04ebdb 1
 
< 0.1%
99b28be3-2ed8-4c6e-8861-767ff5c292d8 1
 
< 0.1%
ef8c5e87-002c-4258-beac-8a3c098adf09 1
 
< 0.1%
2507c1ab-8588-4319-a538-1310243d8f73 1
 
< 0.1%
eacf86e5-dcaa-4fb6-af5a-a881f881951d 1
 
< 0.1%
Other values (547698) 547698
> 99.9%
2025-03-04T14:24:55.560343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2190832
 
11.1%
4 1575724
 
8.0%
8 1164596
 
5.9%
9 1163707
 
5.9%
A 1163311
 
5.9%
B 1162488
 
5.9%
5 1028497
 
5.2%
1 1027869
 
5.2%
E 1027716
 
5.2%
3 1027561
 
5.2%
Other values (9) 7185189
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19717490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2190832
 
11.1%
4 1575724
 
8.0%
8 1164596
 
5.9%
9 1163707
 
5.9%
A 1163311
 
5.9%
B 1162488
 
5.9%
5 1028497
 
5.2%
1 1027869
 
5.2%
E 1027716
 
5.2%
3 1027561
 
5.2%
Other values (9) 7185189
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19717490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2190832
 
11.1%
4 1575724
 
8.0%
8 1164596
 
5.9%
9 1163707
 
5.9%
A 1163311
 
5.9%
B 1162488
 
5.9%
5 1028497
 
5.2%
1 1027869
 
5.2%
E 1027716
 
5.2%
3 1027561
 
5.2%
Other values (9) 7185189
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19717490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2190832
 
11.1%
4 1575724
 
8.0%
8 1164596
 
5.9%
9 1163707
 
5.9%
A 1163311
 
5.9%
B 1162488
 
5.9%
5 1028497
 
5.2%
1 1027869
 
5.2%
E 1027716
 
5.2%
3 1027561
 
5.2%
Other values (9) 7185189
36.4%

catalogNumber
Text

Unique 

Distinct547708
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-03-04T14:24:55.852079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.999998174
Min length7

Characters and Unicode

Total characters4381663
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

Unique547708 ?
Unique (%)100.0%

Sample

1st rowCM000004
2nd rowCM000015
3rd rowCM000018
4th rowCM000026
5th rowCM000029
ValueCountFrequency (%)
cm000004 1
 
< 0.1%
cm000141 1
 
< 0.1%
cm000029 1
 
< 0.1%
cm000037 1
 
< 0.1%
cm000040 1
 
< 0.1%
cm000053 1
 
< 0.1%
cm000065 1
 
< 0.1%
cm000078 1
 
< 0.1%
cm000089 1
 
< 0.1%
cm000100 1
 
< 0.1%
Other values (547698) 547698
> 99.9%
2025-03-04T14:24:56.218226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 547708
12.5%
M 547708
12.5%
3 378772
8.6%
0 378132
8.6%
2 377767
8.6%
4 377696
8.6%
1 376741
8.6%
5 325633
7.4%
8 268305
6.1%
7 268167
6.1%
Other values (4) 535034
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4381663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 547708
12.5%
M 547708
12.5%
3 378772
8.6%
0 378132
8.6%
2 377767
8.6%
4 377696
8.6%
1 376741
8.6%
5 325633
7.4%
8 268305
6.1%
7 268167
6.1%
Other values (4) 535034
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4381663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 547708
12.5%
M 547708
12.5%
3 378772
8.6%
0 378132
8.6%
2 377767
8.6%
4 377696
8.6%
1 376741
8.6%
5 325633
7.4%
8 268305
6.1%
7 268167
6.1%
Other values (4) 535034
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4381663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 547708
12.5%
M 547708
12.5%
3 378772
8.6%
0 378132
8.6%
2 377767
8.6%
4 377696
8.6%
1 376741
8.6%
5 325633
7.4%
8 268305
6.1%
7 268167
6.1%
Other values (4) 535034
12.2%

recordNumber
Text

Missing 

Distinct102160
Distinct (%)19.4%
Missing20723
Missing (%)3.8%
Memory size4.2 MiB
2025-03-04T14:24:56.390434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length4
Mean length4.312115146
Min length1

Characters and Unicode

Total characters2272420
Distinct characters89
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

Unique68944 ?
Unique (%)13.1%

Sample

1st rows.n.
2nd rows.n.
3rd rows.n.
4th rows.n.
5th rows.n.
ValueCountFrequency (%)
s.n 186524
35.2%
1 343
 
0.1%
2 320
 
0.1%
bs 317
 
0.1%
3 295
 
0.1%
4 287
 
0.1%
6 258
 
< 0.1%
10 256
 
< 0.1%
9 253
 
< 0.1%
18 253
 
< 0.1%
Other values (100110) 340797
64.3%
2025-03-04T14:24:56.608531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 373622
16.4%
1 219163
9.6%
s 186753
 
8.2%
n 186716
 
8.2%
2 170189
 
7.5%
3 147488
 
6.5%
4 134344
 
5.9%
8 130025
 
5.7%
9 126991
 
5.6%
0 125919
 
5.5%
Other values (79) 471210
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2272420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 373622
16.4%
1 219163
9.6%
s 186753
 
8.2%
n 186716
 
8.2%
2 170189
 
7.5%
3 147488
 
6.5%
4 134344
 
5.9%
8 130025
 
5.7%
9 126991
 
5.6%
0 125919
 
5.5%
Other values (79) 471210
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2272420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 373622
16.4%
1 219163
9.6%
s 186753
 
8.2%
n 186716
 
8.2%
2 170189
 
7.5%
3 147488
 
6.5%
4 134344
 
5.9%
8 130025
 
5.7%
9 126991
 
5.6%
0 125919
 
5.5%
Other values (79) 471210
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2272420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 373622
16.4%
1 219163
9.6%
s 186753
 
8.2%
n 186716
 
8.2%
2 170189
 
7.5%
3 147488
 
6.5%
4 134344
 
5.9%
8 130025
 
5.7%
9 126991
 
5.6%
0 125919
 
5.5%
Other values (79) 471210
20.7%

recordedBy
Text

Missing 

Distinct30943
Distinct (%)5.7%
Missing8448
Missing (%)1.5%
Memory size4.2 MiB
2025-03-04T14:24:56.752114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length208
Median length166
Mean length17.25474725
Min length1

Characters and Unicode

Total characters9304795
Distinct characters118
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

Unique17892 ?
Unique (%)3.3%

Sample

1st rowJennings, O.E.
2nd rowBuker, W.E.
3rd rowJennings, O.E.
4th rowHenrici, M.
5th rowJennings, O.E.; Jennings, G.K.
ValueCountFrequency (%)
isaac 50866
 
3.3%
jennings 46937
 
3.1%
l.k 36883
 
2.4%
henry 36873
 
2.4%
j.a 35689
 
2.3%
o.e 35322
 
2.3%
f.h 30534
 
2.0%
j 29466
 
1.9%
davis 27660
 
1.8%
b.l 24954
 
1.6%
Other values (16263) 1170163
76.7%
2025-03-04T14:24:56.968444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1256146
 
13.5%
985992
 
10.6%
, 748961
 
8.0%
e 481033
 
5.2%
a 427706
 
4.6%
n 421045
 
4.5%
r 338311
 
3.6%
s 299194
 
3.2%
i 284087
 
3.1%
o 263040
 
2.8%
Other values (108) 3799280
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9304795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1256146
 
13.5%
985992
 
10.6%
, 748961
 
8.0%
e 481033
 
5.2%
a 427706
 
4.6%
n 421045
 
4.5%
r 338311
 
3.6%
s 299194
 
3.2%
i 284087
 
3.1%
o 263040
 
2.8%
Other values (108) 3799280
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9304795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1256146
 
13.5%
985992
 
10.6%
, 748961
 
8.0%
e 481033
 
5.2%
a 427706
 
4.6%
n 421045
 
4.5%
r 338311
 
3.6%
s 299194
 
3.2%
i 284087
 
3.1%
o 263040
 
2.8%
Other values (108) 3799280
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9304795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1256146
 
13.5%
985992
 
10.6%
, 748961
 
8.0%
e 481033
 
5.2%
a 427706
 
4.6%
n 421045
 
4.5%
r 338311
 
3.6%
s 299194
 
3.2%
i 284087
 
3.1%
o 263040
 
2.8%
Other values (108) 3799280
40.8%

reproductiveCondition
Text

Missing 

Distinct48
Distinct (%)< 0.1%
Missing110994
Missing (%)20.3%
Memory size4.2 MiB
2025-03-04T14:24:57.011876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.272897136
Min length1

Characters and Unicode

Total characters1429320
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

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowveg
2nd rowveg
3rd rowveg
4th rowveg
5th rowspores
ValueCountFrequency (%)
fl 148046
33.5%
fr 102280
23.1%
fl-fr 92710
21.0%
fl,fr 36969
 
8.4%
veg 27299
 
6.2%
spores 25247
 
5.7%
buds 5405
 
1.2%
cones 3520
 
0.8%
425
 
0.1%
bulbil 149
 
< 0.1%
Other values (24) 121
 
< 0.1%
2025-03-04T14:24:57.099262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 509660
35.7%
l 278012
19.5%
r 257199
18.0%
- 92718
 
6.5%
s 59504
 
4.2%
e 56068
 
3.9%
, 36973
 
2.6%
o 28775
 
2.0%
g 27301
 
1.9%
v 27293
 
1.9%
Other values (25) 55817
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1429320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 509660
35.7%
l 278012
19.5%
r 257199
18.0%
- 92718
 
6.5%
s 59504
 
4.2%
e 56068
 
3.9%
, 36973
 
2.6%
o 28775
 
2.0%
g 27301
 
1.9%
v 27293
 
1.9%
Other values (25) 55817
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1429320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 509660
35.7%
l 278012
19.5%
r 257199
18.0%
- 92718
 
6.5%
s 59504
 
4.2%
e 56068
 
3.9%
, 36973
 
2.6%
o 28775
 
2.0%
g 27301
 
1.9%
v 27293
 
1.9%
Other values (25) 55817
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1429320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 509660
35.7%
l 278012
19.5%
r 257199
18.0%
- 92718
 
6.5%
s 59504
 
4.2%
e 56068
 
3.9%
, 36973
 
2.6%
o 28775
 
2.0%
g 27301
 
1.9%
v 27293
 
1.9%
Other values (25) 55817
 
3.9%

establishmentMeans
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:24:57.128012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
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 (%)100.0%

Sample

1st rowcultivated
ValueCountFrequency (%)
cultivated 1
100.0%
2025-03-04T14:24:57.210459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2
20.0%
c 1
10.0%
u 1
10.0%
l 1
10.0%
i 1
10.0%
v 1
10.0%
a 1
10.0%
e 1
10.0%
d 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2
20.0%
c 1
10.0%
u 1
10.0%
l 1
10.0%
i 1
10.0%
v 1
10.0%
a 1
10.0%
e 1
10.0%
d 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2
20.0%
c 1
10.0%
u 1
10.0%
l 1
10.0%
i 1
10.0%
v 1
10.0%
a 1
10.0%
e 1
10.0%
d 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2
20.0%
c 1
10.0%
u 1
10.0%
l 1
10.0%
i 1
10.0%
v 1
10.0%
a 1
10.0%
e 1
10.0%
d 1
10.0%
Distinct9
Distinct (%)< 0.1%
Missing449157
Missing (%)82.0%
Memory size4.2 MiB
2025-03-04T14:24:57.241523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length30
Median length26
Mean length23.38622642
Min length2

Characters and Unicode

Total characters2304736
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowreviewed - high confidence
2nd rowreviewed - high confidence
3rd rowreviewed - high confidence
4th rowreviewed - high confidence
5th rowreviewed - high confidence
ValueCountFrequency (%)
87419
24.2%
reviewed 87305
24.2%
high 87305
24.2%
confidence 87305
24.2%
bli 9235
 
2.6%
fix 1999
 
0.6%
check 114
 
< 0.1%
shell 5
 
< 0.1%
map 5
 
< 0.1%
oil 5
 
< 0.1%
Other values (11) 26
 
< 0.1%
2025-03-04T14:24:57.329821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 436661
18.9%
i 263921
11.5%
262172
11.4%
c 174839
7.6%
h 174729
7.6%
n 174619
 
7.6%
d 174613
 
7.6%
f 89305
 
3.9%
o 87314
 
3.8%
r 87311
 
3.8%
Other values (28) 379252
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2304736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 436661
18.9%
i 263921
11.5%
262172
11.4%
c 174839
7.6%
h 174729
7.6%
n 174619
 
7.6%
d 174613
 
7.6%
f 89305
 
3.9%
o 87314
 
3.8%
r 87311
 
3.8%
Other values (28) 379252
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2304736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 436661
18.9%
i 263921
11.5%
262172
11.4%
c 174839
7.6%
h 174729
7.6%
n 174619
 
7.6%
d 174613
 
7.6%
f 89305
 
3.9%
o 87314
 
3.8%
r 87311
 
3.8%
Other values (28) 379252
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2304736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 436661
18.9%
i 263921
11.5%
262172
11.4%
c 174839
7.6%
h 174729
7.6%
n 174619
 
7.6%
d 174613
 
7.6%
f 89305
 
3.9%
o 87314
 
3.8%
r 87311
 
3.8%
Other values (28) 379252
16.5%

preparations
Text

Missing 

Distinct2
Distinct (%)0.2%
Missing546820
Missing (%)99.8%
Memory size4.2 MiB
2025-03-04T14:24:57.362288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length10
Mean length10.02702703
Min length10

Characters and Unicode

Total characters8904
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowin ethanol
2nd rowin ethanol
3rd rowin ethanol
4th rowin ethanol
5th rowin ethanol
ValueCountFrequency (%)
in 887
49.8%
ethanol 887
49.8%
uinta 1
 
0.1%
basin 1
 
0.1%
uteland 1
 
0.1%
mine 1
 
0.1%
green 1
 
0.1%
r 1
 
0.1%
2025-03-04T14:24:57.447016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1779
20.0%
892
10.0%
e 891
10.0%
a 890
10.0%
i 889
10.0%
t 889
10.0%
l 888
10.0%
h 887
10.0%
o 887
10.0%
M 1
 
< 0.1%
Other values (11) 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1779
20.0%
892
10.0%
e 891
10.0%
a 890
10.0%
i 889
10.0%
t 889
10.0%
l 888
10.0%
h 887
10.0%
o 887
10.0%
M 1
 
< 0.1%
Other values (11) 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1779
20.0%
892
10.0%
e 891
10.0%
a 890
10.0%
i 889
10.0%
t 889
10.0%
l 888
10.0%
h 887
10.0%
o 887
10.0%
M 1
 
< 0.1%
Other values (11) 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1779
20.0%
892
10.0%
e 891
10.0%
a 890
10.0%
i 889
10.0%
t 889
10.0%
l 888
10.0%
h 887
10.0%
o 887
10.0%
M 1
 
< 0.1%
Other values (11) 11
 
0.1%

disposition
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:24:57.480119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters8
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 row27-Jun-03
ValueCountFrequency (%)
27-jun-03 1
100.0%
2025-03-04T14:24:57.563200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2
22.2%
2 1
11.1%
7 1
11.1%
J 1
11.1%
u 1
11.1%
n 1
11.1%
0 1
11.1%
3 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2
22.2%
2 1
11.1%
7 1
11.1%
J 1
11.1%
u 1
11.1%
n 1
11.1%
0 1
11.1%
3 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2
22.2%
2 1
11.1%
7 1
11.1%
J 1
11.1%
u 1
11.1%
n 1
11.1%
0 1
11.1%
3 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2
22.2%
2 1
11.1%
7 1
11.1%
J 1
11.1%
u 1
11.1%
n 1
11.1%
0 1
11.1%
3 1
11.1%

associatedOccurrences
Text

Missing 

Distinct44395
Distinct (%)100.0%
Missing503313
Missing (%)91.9%
Memory size4.2 MiB
2025-03-04T14:24:57.690144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14951
Median length8467
Mean length778.4214213
Min length197

Characters and Unicode

Total characters34558019
Distinct characters87
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

Unique44395 ?
Unique (%)100.0%

Sample

1st rowrelationship: herbariumSpecimenDuplicate, identifier: 4b543a49-a5b9-451e-b98d-e6cd658b5f49, scientificName: Equisetum arvense, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=4b543a49-a5b9-451e-b98d-e6cd658b5f49
2nd rowrelationship: herbariumSpecimenDuplicate, identifier: 92958f9b-d7df-48b6-ac64-7c33595518f4, scientificName: Equisetum fluviatile, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=92958f9b-d7df-48b6-ac64-7c33595518f4 | relationship: herbariumSpecimenDuplicate, identifier: 5c96e9a8-cde5-4698-8763-d693e51fd135, scientificName: Equisetum fluviatile, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=5c96e9a8-cde5-4698-8763-d693e51fd135
3rd rowrelationship: herbariumSpecimenDuplicate, identifier: fd93d426-66ae-47d6-b276-eaf2d0b45982, scientificName: Equisetum arvense var. boreale, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=fd93d426-66ae-47d6-b276-eaf2d0b45982 | relationship: herbariumSpecimenDuplicate, identifier: 1976A985-2FA7-4941-BC78-67870B6FA422, scientificName: Equisetum arvense var. boreale, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=1976A985-2FA7-4941-BC78-67870B6FA422 | relationship: herbariumSpecimenDuplicate, identifier: 7f817c26-e757-47c5-9fd3-3b5b1b5ea61c, scientificName: Equisetum arvense var. boreale, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=7f817c26-e757-47c5-9fd3-3b5b1b5ea61c | relationship: herbariumSpecimenDuplicate, identifier: 196fb190-1918-4aa5-9b91-fee12c61ab8d, scientificName: Equisetum arvense var. boreale, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=196fb190-1918-4aa5-9b91-fee12c61ab8d | relationship: herbariumSpecimenDuplicate, identifier: 1e8704dc-c7e9-41a8-9c73-20973aabe9aa, scientificName: Equisetum arvense var. boreale, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=1e8704dc-c7e9-41a8-9c73-20973aabe9aa
4th rowrelationship: herbariumSpecimenDuplicate, identifier: 92d2e44a-4f2f-4084-aedf-89afd790e727, scientificName: Equisetum arvense, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=92d2e44a-4f2f-4084-aedf-89afd790e727 | relationship: herbariumSpecimenDuplicate, identifier: ec2c5cdf-cc35-4c94-affb-5ea41d42dd23, scientificName: Equisetum arvense, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=ec2c5cdf-cc35-4c94-affb-5ea41d42dd23 | relationship: herbariumSpecimenDuplicate, identifier: 0fcce954-3c8c-4b97-bbf8-f9f87b7e10e2, scientificName: Equisetum arvense, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=0fcce954-3c8c-4b97-bbf8-f9f87b7e10e2
5th rowrelationship: herbariumSpecimenDuplicate, identifier: 060c9c4b-8e00-43c5-8080-67f9e8586c8e, scientificName: Equisetum praealtum, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=060c9c4b-8e00-43c5-8080-67f9e8586c8e | relationship: herbariumSpecimenDuplicate, identifier: 064ed3c0-9194-4ccc-acab-789154c2a2be, scientificName: Equisetum hyemale, resourceUrl: https://midatlanticherbaria.org/portal/collections/individual/index.php?guid=064ed3c0-9194-4ccc-acab-789154c2a2be
ValueCountFrequency (%)
relationship 134248
 
10.1%
identifier 134248
 
10.1%
herbariumspecimenduplicate 134248
 
10.1%
resourceurl 134248
 
10.1%
scientificname 134125
 
10.1%
89857
 
6.7%
var 13100
 
1.0%
carex 8199
 
0.6%
subsp 3861
 
0.3%
juncus 1632
 
0.1%
Other values (269231) 543778
40.8%
2025-03-04T14:24:57.897152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2959588
 
8.6%
e 2494447
 
7.2%
a 2168032
 
6.3%
r 1697624
 
4.9%
c 1633443
 
4.7%
t 1479283
 
4.3%
1421397
 
4.1%
d 1267163
 
3.7%
l 1241923
 
3.6%
n 1234699
 
3.6%
Other values (77) 16960420
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34558019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2959588
 
8.6%
e 2494447
 
7.2%
a 2168032
 
6.3%
r 1697624
 
4.9%
c 1633443
 
4.7%
t 1479283
 
4.3%
1421397
 
4.1%
d 1267163
 
3.7%
l 1241923
 
3.6%
n 1234699
 
3.6%
Other values (77) 16960420
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34558019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2959588
 
8.6%
e 2494447
 
7.2%
a 2168032
 
6.3%
r 1697624
 
4.9%
c 1633443
 
4.7%
t 1479283
 
4.3%
1421397
 
4.1%
d 1267163
 
3.7%
l 1241923
 
3.6%
n 1234699
 
3.6%
Other values (77) 16960420
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34558019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2959588
 
8.6%
e 2494447
 
7.2%
a 2168032
 
6.3%
r 1697624
 
4.9%
c 1633443
 
4.7%
t 1479283
 
4.3%
1421397
 
4.1%
d 1267163
 
3.7%
l 1241923
 
3.6%
n 1234699
 
3.6%
Other values (77) 16960420
49.1%

associatedTaxa
Text

Missing 

Distinct29978
Distinct (%)67.5%
Missing503313
Missing (%)91.9%
Memory size4.2 MiB
2025-03-04T14:24:57.931935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3181
Median length1959
Mean length151.8361077
Min length26

Characters and Unicode

Total characters6740764
Distinct characters79
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

Unique23721 ?
Unique (%)53.4%

Sample

1st rowherbariumSpecimenDuplicate: Equisetum arvense
2nd rowherbariumSpecimenDuplicate: Equisetum fluviatile | herbariumSpecimenDuplicate: Equisetum fluviatile
3rd rowherbariumSpecimenDuplicate: Equisetum arvense var. boreale | herbariumSpecimenDuplicate: Equisetum arvense var. boreale | herbariumSpecimenDuplicate: Equisetum arvense var. boreale | herbariumSpecimenDuplicate: Equisetum arvense var. boreale | herbariumSpecimenDuplicate: Equisetum arvense var. boreale
4th rowherbariumSpecimenDuplicate: Equisetum arvense | herbariumSpecimenDuplicate: Equisetum arvense | herbariumSpecimenDuplicate: Equisetum arvense
5th rowherbariumSpecimenDuplicate: Equisetum praealtum | herbariumSpecimenDuplicate: Equisetum hyemale
ValueCountFrequency (%)
herbariumspecimenduplicate 134248
25.5%
89857
 
17.1%
var 13100
 
2.5%
carex 8199
 
1.6%
subsp 3861
 
0.7%
juncus 1632
 
0.3%
astragalus 1531
 
0.3%
rubus 1390
 
0.3%
penstemon 1366
 
0.3%
salix 1363
 
0.3%
Other values (11308) 269632
51.2%
2025-03-04T14:24:58.037558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 711318
 
10.6%
i 660312
 
9.8%
a 586519
 
8.7%
481784
 
7.1%
r 450746
 
6.7%
u 420386
 
6.2%
c 370096
 
5.5%
m 363854
 
5.4%
p 342320
 
5.1%
l 286356
 
4.2%
Other values (69) 2067073
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6740764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 711318
 
10.6%
i 660312
 
9.8%
a 586519
 
8.7%
481784
 
7.1%
r 450746
 
6.7%
u 420386
 
6.2%
c 370096
 
5.5%
m 363854
 
5.4%
p 342320
 
5.1%
l 286356
 
4.2%
Other values (69) 2067073
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6740764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 711318
 
10.6%
i 660312
 
9.8%
a 586519
 
8.7%
481784
 
7.1%
r 450746
 
6.7%
u 420386
 
6.2%
c 370096
 
5.5%
m 363854
 
5.4%
p 342320
 
5.1%
l 286356
 
4.2%
Other values (69) 2067073
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6740764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 711318
 
10.6%
i 660312
 
9.8%
a 586519
 
8.7%
481784
 
7.1%
r 450746
 
6.7%
u 420386
 
6.2%
c 370096
 
5.5%
m 363854
 
5.4%
p 342320
 
5.1%
l 286356
 
4.2%
Other values (69) 2067073
30.7%

otherCatalogNumbers
Text

Missing 

Distinct10
Distinct (%)90.9%
Missing547697
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:24:58.075598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length6.727272727
Min length1

Characters and Unicode

Total characters74
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

Unique9 ?
Unique (%)81.8%

Sample

1st rowCM542527
2nd rowCM543236
3rd rowCM542558
4th rowCM543235
5th rowCM542547
ValueCountFrequency (%)
x 2
18.2%
cm542527 1
9.1%
cm543236 1
9.1%
cm542558 1
9.1%
cm543235 1
9.1%
cm542547 1
9.1%
cm543234 1
9.1%
cm542526 1
9.1%
cm542548 1
9.1%
cm542885 1
9.1%
2025-03-04T14:24:58.167490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 17
23.0%
4 12
16.2%
2 11
14.9%
C 9
12.2%
M 9
12.2%
3 6
 
8.1%
8 4
 
5.4%
x 2
 
2.7%
7 2
 
2.7%
6 2
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 17
23.0%
4 12
16.2%
2 11
14.9%
C 9
12.2%
M 9
12.2%
3 6
 
8.1%
8 4
 
5.4%
x 2
 
2.7%
7 2
 
2.7%
6 2
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 17
23.0%
4 12
16.2%
2 11
14.9%
C 9
12.2%
M 9
12.2%
3 6
 
8.1%
8 4
 
5.4%
x 2
 
2.7%
7 2
 
2.7%
6 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 17
23.0%
4 12
16.2%
2 11
14.9%
C 9
12.2%
M 9
12.2%
3 6
 
8.1%
8 4
 
5.4%
x 2
 
2.7%
7 2
 
2.7%
6 2
 
2.7%

occurrenceRemarks
Text

Missing 

Distinct44
Distinct (%)41.1%
Missing547601
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:24:58.213296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length105
Median length78
Mean length24.3364486
Min length3

Characters and Unicode

Total characters2604
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

Unique35 ?
Unique (%)32.7%

Sample

1st rowlabel reads 5 Jul 1858, tag on plant has 23 Jan 1866
2nd rowlabel data is suspect
3rd rowSCW
4th rowSCW
5th roworiginal label had Indiana County
ValueCountFrequency (%)
32
 
6.9%
burdickii 26
 
5.6%
spgrund 26
 
5.6%
2016 26
 
5.6%
scw 19
 
4.1%
sheet 16
 
3.4%
label 12
 
2.6%
is 11
 
2.4%
of 10
 
2.2%
data 9
 
1.9%
Other values (158) 277
59.7%
2025-03-04T14:24:58.324451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
357
 
13.7%
e 161
 
6.2%
i 151
 
5.8%
a 130
 
5.0%
d 110
 
4.2%
r 109
 
4.2%
t 109
 
4.2%
s 104
 
4.0%
l 90
 
3.5%
n 88
 
3.4%
Other values (49) 1195
45.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
357
 
13.7%
e 161
 
6.2%
i 151
 
5.8%
a 130
 
5.0%
d 110
 
4.2%
r 109
 
4.2%
t 109
 
4.2%
s 104
 
4.0%
l 90
 
3.5%
n 88
 
3.4%
Other values (49) 1195
45.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
357
 
13.7%
e 161
 
6.2%
i 151
 
5.8%
a 130
 
5.0%
d 110
 
4.2%
r 109
 
4.2%
t 109
 
4.2%
s 104
 
4.0%
l 90
 
3.5%
n 88
 
3.4%
Other values (49) 1195
45.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
357
 
13.7%
e 161
 
6.2%
i 151
 
5.8%
a 130
 
5.0%
d 110
 
4.2%
r 109
 
4.2%
t 109
 
4.2%
s 104
 
4.0%
l 90
 
3.5%
n 88
 
3.4%
Other values (49) 1195
45.9%

eventDate
Text

Missing 

Distinct39214
Distinct (%)7.5%
Missing27372
Missing (%)5.0%
Memory size4.2 MiB
2025-03-04T14:24:58.463283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.559578811
Min length4

Characters and Unicode

Total characters4974193
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

Unique7705 ?
Unique (%)1.5%

Sample

1st row1922-08-19
2nd row1970-07-29
3rd row1941-06-15
4th row1951-07-14
5th row1915-05-08
ValueCountFrequency (%)
1840 2575
 
0.5%
1922-10 817
 
0.2%
1924 542
 
0.1%
1922 520
 
0.1%
1920-08 439
 
0.1%
1917 423
 
0.1%
1874 420
 
0.1%
1930-07 397
 
0.1%
1880 393
 
0.1%
1981 383
 
0.1%
Other values (39204) 513427
98.7%
2025-03-04T14:24:58.682955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 964283
19.4%
1 828386
16.7%
0 826102
16.6%
9 646712
13.0%
2 362698
 
7.3%
8 319097
 
6.4%
7 247663
 
5.0%
6 218081
 
4.4%
5 210604
 
4.2%
3 177324
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4974193
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 964283
19.4%
1 828386
16.7%
0 826102
16.6%
9 646712
13.0%
2 362698
 
7.3%
8 319097
 
6.4%
7 247663
 
5.0%
6 218081
 
4.4%
5 210604
 
4.2%
3 177324
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4974193
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 964283
19.4%
1 828386
16.7%
0 826102
16.6%
9 646712
13.0%
2 362698
 
7.3%
8 319097
 
6.4%
7 247663
 
5.0%
6 218081
 
4.4%
5 210604
 
4.2%
3 177324
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4974193
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 964283
19.4%
1 828386
16.7%
0 826102
16.6%
9 646712
13.0%
2 362698
 
7.3%
8 319097
 
6.4%
7 247663
 
5.0%
6 218081
 
4.4%
5 210604
 
4.2%
3 177324
 
3.6%

startDayOfYear
Text

Missing 

Distinct366
Distinct (%)0.1%
Missing83521
Missing (%)15.2%
Memory size4.2 MiB
2025-03-04T14:24:58.834332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.935857747
Min length1

Characters and Unicode

Total characters1362787
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 row231
2nd row210
3rd row166
4th row195
5th row128
ValueCountFrequency (%)
196 3667
 
0.8%
206 3530
 
0.8%
175 3467
 
0.7%
197 3458
 
0.7%
203 3445
 
0.7%
169 3413
 
0.7%
174 3378
 
0.7%
208 3377
 
0.7%
207 3310
 
0.7%
201 3289
 
0.7%
Other values (356) 429853
92.6%
2025-03-04T14:24:59.049481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 313961
23.0%
2 296095
21.7%
3 112537
 
8.3%
4 93917
 
6.9%
6 93513
 
6.9%
5 92970
 
6.8%
7 92134
 
6.8%
0 91404
 
6.7%
9 88759
 
6.5%
8 87497
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1362787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 313961
23.0%
2 296095
21.7%
3 112537
 
8.3%
4 93917
 
6.9%
6 93513
 
6.9%
5 92970
 
6.8%
7 92134
 
6.8%
0 91404
 
6.7%
9 88759
 
6.5%
8 87497
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1362787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 313961
23.0%
2 296095
21.7%
3 112537
 
8.3%
4 93917
 
6.9%
6 93513
 
6.9%
5 92970
 
6.8%
7 92134
 
6.8%
0 91404
 
6.7%
9 88759
 
6.5%
8 87497
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1362787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 313961
23.0%
2 296095
21.7%
3 112537
 
8.3%
4 93917
 
6.9%
6 93513
 
6.9%
5 92970
 
6.8%
7 92134
 
6.8%
0 91404
 
6.7%
9 88759
 
6.5%
8 87497
 
6.4%

endDayOfYear
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing547704
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:24:59.095006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
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

Unique4 ?
Unique (%)100.0%

Sample

1st row149
2nd row140
3rd row215
4th row227
ValueCountFrequency (%)
149 1
25.0%
140 1
25.0%
215 1
25.0%
227 1
25.0%
2025-03-04T14:24:59.177094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3
25.0%
2 3
25.0%
4 2
16.7%
9 1
 
8.3%
0 1
 
8.3%
5 1
 
8.3%
7 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3
25.0%
2 3
25.0%
4 2
16.7%
9 1
 
8.3%
0 1
 
8.3%
5 1
 
8.3%
7 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3
25.0%
2 3
25.0%
4 2
16.7%
9 1
 
8.3%
0 1
 
8.3%
5 1
 
8.3%
7 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3
25.0%
2 3
25.0%
4 2
16.7%
9 1
 
8.3%
0 1
 
8.3%
5 1
 
8.3%
7 1
 
8.3%

year
Text

Missing 

Distinct243
Distinct (%)< 0.1%
Missing16300
Missing (%)3.0%
Memory size4.2 MiB
2025-03-04T14:24:59.315955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.999984946
Min length1

Characters and Unicode

Total characters2125624
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

Unique16 ?
Unique (%)< 0.1%

Sample

1st row1922
2nd row1970
3rd row1941
4th row1951
5th row1915
ValueCountFrequency (%)
1994 8948
 
1.7%
1982 8307
 
1.6%
1979 8266
 
1.6%
1992 8127
 
1.5%
1984 7849
 
1.5%
1993 7830
 
1.5%
1980 7390
 
1.4%
1991 7004
 
1.3%
1987 6978
 
1.3%
1985 6669
 
1.3%
Other values (233) 454040
85.4%
2025-03-04T14:24:59.532285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 578094
27.2%
9 555707
26.1%
8 188654
 
8.9%
0 166337
 
7.8%
2 152057
 
7.2%
7 103188
 
4.9%
4 99031
 
4.7%
3 97415
 
4.6%
5 96857
 
4.6%
6 88284
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2125624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 578094
27.2%
9 555707
26.1%
8 188654
 
8.9%
0 166337
 
7.8%
2 152057
 
7.2%
7 103188
 
4.9%
4 99031
 
4.7%
3 97415
 
4.6%
5 96857
 
4.6%
6 88284
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2125624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 578094
27.2%
9 555707
26.1%
8 188654
 
8.9%
0 166337
 
7.8%
2 152057
 
7.2%
7 103188
 
4.9%
4 99031
 
4.7%
3 97415
 
4.6%
5 96857
 
4.6%
6 88284
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2125624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 578094
27.2%
9 555707
26.1%
8 188654
 
8.9%
0 166337
 
7.8%
2 152057
 
7.2%
7 103188
 
4.9%
4 99031
 
4.7%
3 97415
 
4.6%
5 96857
 
4.6%
6 88284
 
4.2%

month
Text

Missing 

Distinct13
Distinct (%)< 0.1%
Missing23216
Missing (%)4.2%
Memory size4.2 MiB
2025-03-04T14:24:59.581904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.07745018
Min length1

Characters and Unicode

Total characters565114
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 row8
2nd row7
3rd row6
4th row7
5th row5
ValueCountFrequency (%)
7 101552
19.4%
8 88835
16.9%
6 86689
16.5%
5 70850
13.5%
9 57927
11.0%
4 30752
 
5.9%
10 25686
 
4.9%
0 20720
 
4.0%
3 12914
 
2.5%
11 8012
 
1.5%
Other values (3) 20555
 
3.9%
2025-03-04T14:24:59.667338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 101552
18.0%
8 88835
15.7%
6 86689
15.3%
5 70850
12.5%
9 57927
10.3%
1 55276
9.8%
0 46406
8.2%
4 30752
 
5.4%
2 13913
 
2.5%
3 12914
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 101552
18.0%
8 88835
15.7%
6 86689
15.3%
5 70850
12.5%
9 57927
10.3%
1 55276
9.8%
0 46406
8.2%
4 30752
 
5.4%
2 13913
 
2.5%
3 12914
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 101552
18.0%
8 88835
15.7%
6 86689
15.3%
5 70850
12.5%
9 57927
10.3%
1 55276
9.8%
0 46406
8.2%
4 30752
 
5.4%
2 13913
 
2.5%
3 12914
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 101552
18.0%
8 88835
15.7%
6 86689
15.3%
5 70850
12.5%
9 57927
10.3%
1 55276
9.8%
0 46406
8.2%
4 30752
 
5.4%
2 13913
 
2.5%
3 12914
 
2.3%

day
Text

Missing 

Distinct32
Distinct (%)< 0.1%
Missing25750
Missing (%)4.7%
Memory size4.2 MiB
2025-03-04T14:24:59.711963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.639712391
Min length1

Characters and Unicode

Total characters855861
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 row19
2nd row29
3rd row15
4th row14
5th row8
ValueCountFrequency (%)
0 57207
 
11.0%
22 16687
 
3.2%
20 16510
 
3.2%
15 16447
 
3.2%
17 16101
 
3.1%
24 16079
 
3.1%
30 15826
 
3.0%
10 15776
 
3.0%
16 15755
 
3.0%
23 15622
 
3.0%
Other values (22) 319948
61.3%
2025-03-04T14:24:59.814984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 207802
24.3%
2 201345
23.5%
0 105319
12.3%
3 69046
 
8.1%
8 45845
 
5.4%
4 45792
 
5.4%
6 45674
 
5.3%
7 45554
 
5.3%
5 45466
 
5.3%
9 44018
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 855861
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 207802
24.3%
2 201345
23.5%
0 105319
12.3%
3 69046
 
8.1%
8 45845
 
5.4%
4 45792
 
5.4%
6 45674
 
5.3%
7 45554
 
5.3%
5 45466
 
5.3%
9 44018
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 855861
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 207802
24.3%
2 201345
23.5%
0 105319
12.3%
3 69046
 
8.1%
8 45845
 
5.4%
4 45792
 
5.4%
6 45674
 
5.3%
7 45554
 
5.3%
5 45466
 
5.3%
9 44018
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 855861
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 207802
24.3%
2 201345
23.5%
0 105319
12.3%
3 69046
 
8.1%
8 45845
 
5.4%
4 45792
 
5.4%
6 45674
 
5.3%
7 45554
 
5.3%
5 45466
 
5.3%
9 44018
 
5.1%

verbatimEventDate
Text

Missing 

Distinct55499
Distinct (%)11.8%
Missing76931
Missing (%)14.0%
Memory size4.2 MiB
2025-03-04T14:24:59.959572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length66
Median length11
Mean length10.03653747
Min length1

Characters and Unicode

Total characters4724971
Distinct characters82
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

Unique19077 ?
Unique (%)4.1%

Sample

1st row19 Aug 1922
2nd row29 Jul 1970
3rd row15 Jun 1941
4th row14 Jul 1951
5th row8 May 1915
ValueCountFrequency (%)
jul 74039
 
6.1%
jun 63968
 
5.3%
aug 62216
 
5.2%
may 53710
 
4.4%
sep 38300
 
3.2%
apr 23258
 
1.9%
oct 16814
 
1.4%
22 12720
 
1.1%
20 12592
 
1.0%
17 12491
 
1.0%
Other values (20697) 837004
69.3%
2025-03-04T14:25:00.186191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
736335
15.6%
1 703638
14.9%
9 511294
 
10.8%
2 305093
 
6.5%
8 227029
 
4.8%
u 215145
 
4.6%
0 159587
 
3.4%
3 155426
 
3.3%
J 150622
 
3.2%
7 146958
 
3.1%
Other values (72) 1413844
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4724971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
736335
15.6%
1 703638
14.9%
9 511294
 
10.8%
2 305093
 
6.5%
8 227029
 
4.8%
u 215145
 
4.6%
0 159587
 
3.4%
3 155426
 
3.3%
J 150622
 
3.2%
7 146958
 
3.1%
Other values (72) 1413844
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4724971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
736335
15.6%
1 703638
14.9%
9 511294
 
10.8%
2 305093
 
6.5%
8 227029
 
4.8%
u 215145
 
4.6%
0 159587
 
3.4%
3 155426
 
3.3%
J 150622
 
3.2%
7 146958
 
3.1%
Other values (72) 1413844
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4724971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
736335
15.6%
1 703638
14.9%
9 511294
 
10.8%
2 305093
 
6.5%
8 227029
 
4.8%
u 215145
 
4.6%
0 159587
 
3.4%
3 155426
 
3.3%
J 150622
 
3.2%
7 146958
 
3.1%
Other values (72) 1413844
29.9%

habitat
Text

Missing 

Distinct112580
Distinct (%)36.4%
Missing238673
Missing (%)43.6%
Memory size4.2 MiB
2025-03-04T14:25:00.324697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length104769
Median length181
Mean length23.30890676
Min length1

Characters and Unicode

Total characters7203268
Distinct characters142
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

Unique86611 ?
Unique (%)28.0%

Sample

1st rowmountain top
2nd rowmoist sandy places
3rd rowrailroad along meadow
4th rowedge of woods
5th rowwooded bog
ValueCountFrequency (%)
in 46793
 
4.0%
forest 38613
 
3.3%
woods 35762
 
3.1%
of 34442
 
3.0%
on 25630
 
2.2%
along 22255
 
1.9%
open 21259
 
1.8%
roadside 19209
 
1.7%
17268
 
1.5%
slope 15405
 
1.3%
Other values (24528) 883565
76.2%
2025-03-04T14:25:00.547515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
846823
11.8%
e 665280
 
9.2%
o 623651
 
8.7%
a 523492
 
7.3%
s 487429
 
6.8%
d 433310
 
6.0%
r 427985
 
5.9%
n 394510
 
5.5%
i 392185
 
5.4%
l 328726
 
4.6%
Other values (132) 2079877
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7203268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
846823
11.8%
e 665280
 
9.2%
o 623651
 
8.7%
a 523492
 
7.3%
s 487429
 
6.8%
d 433310
 
6.0%
r 427985
 
5.9%
n 394510
 
5.5%
i 392185
 
5.4%
l 328726
 
4.6%
Other values (132) 2079877
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7203268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
846823
11.8%
e 665280
 
9.2%
o 623651
 
8.7%
a 523492
 
7.3%
s 487429
 
6.8%
d 433310
 
6.0%
r 427985
 
5.9%
n 394510
 
5.5%
i 392185
 
5.4%
l 328726
 
4.6%
Other values (132) 2079877
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7203268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
846823
11.8%
e 665280
 
9.2%
o 623651
 
8.7%
a 523492
 
7.3%
s 487429
 
6.8%
d 433310
 
6.0%
r 427985
 
5.9%
n 394510
 
5.5%
i 392185
 
5.4%
l 328726
 
4.6%
Other values (132) 2079877
28.9%

samplingProtocol
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:00.591973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
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 rowUintah
ValueCountFrequency (%)
uintah 1
100.0%
2025-03-04T14:25:00.669888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1
16.7%
i 1
16.7%
n 1
16.7%
t 1
16.7%
a 1
16.7%
h 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1
16.7%
i 1
16.7%
n 1
16.7%
t 1
16.7%
a 1
16.7%
h 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1
16.7%
i 1
16.7%
n 1
16.7%
t 1
16.7%
a 1
16.7%
h 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1
16.7%
i 1
16.7%
n 1
16.7%
t 1
16.7%
a 1
16.7%
h 1
16.7%

sampleSizeValue
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:00.697512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
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 row152
ValueCountFrequency (%)
152 1
100.0%
2025-03-04T14:25:00.778104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
2 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
2 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
2 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
2 1
33.3%

samplingEffort
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:00.806419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
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 row~500 ft
ValueCountFrequency (%)
500 1
50.0%
ft 1
50.0%
2025-03-04T14:25:00.886408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2
28.6%
~ 1
14.3%
5 1
14.3%
1
14.3%
f 1
14.3%
t 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
28.6%
~ 1
14.3%
5 1
14.3%
1
14.3%
f 1
14.3%
t 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
28.6%
~ 1
14.3%
5 1
14.3%
1
14.3%
f 1
14.3%
t 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
28.6%
~ 1
14.3%
5 1
14.3%
1
14.3%
f 1
14.3%
t 1
14.3%

higherGeographyID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:00.916374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
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 rowBonnie L. Isaac
ValueCountFrequency (%)
bonnie 1
33.3%
l 1
33.3%
isaac 1
33.3%
2025-03-04T14:25:01.001433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

islandGroup
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:01.032142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters33
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowTranslated from given coordinates
ValueCountFrequency (%)
translated 1
25.0%
from 1
25.0%
given 1
25.0%
coordinates 1
25.0%
2025-03-04T14:25:01.118628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
9.1%
n 3
9.1%
e 3
9.1%
r 3
9.1%
3
9.1%
o 3
9.1%
d 2
 
6.1%
s 2
 
6.1%
t 2
 
6.1%
i 2
 
6.1%
Other values (7) 7
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
9.1%
n 3
9.1%
e 3
9.1%
r 3
9.1%
3
9.1%
o 3
9.1%
d 2
 
6.1%
s 2
 
6.1%
t 2
 
6.1%
i 2
 
6.1%
Other values (7) 7
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
9.1%
n 3
9.1%
e 3
9.1%
r 3
9.1%
3
9.1%
o 3
9.1%
d 2
 
6.1%
s 2
 
6.1%
t 2
 
6.1%
i 2
 
6.1%
Other values (7) 7
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
9.1%
n 3
9.1%
e 3
9.1%
r 3
9.1%
3
9.1%
o 3
9.1%
d 2
 
6.1%
s 2
 
6.1%
t 2
 
6.1%
i 2
 
6.1%
Other values (7) 7
21.2%

island
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:01.150311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
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 row40.130056
ValueCountFrequency (%)
40.130056 1
100.0%
2025-03-04T14:25:01.246162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3
33.3%
4 1
 
11.1%
. 1
 
11.1%
1 1
 
11.1%
3 1
 
11.1%
5 1
 
11.1%
6 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3
33.3%
4 1
 
11.1%
. 1
 
11.1%
1 1
 
11.1%
3 1
 
11.1%
5 1
 
11.1%
6 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3
33.3%
4 1
 
11.1%
. 1
 
11.1%
1 1
 
11.1%
3 1
 
11.1%
5 1
 
11.1%
6 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3
33.3%
4 1
 
11.1%
. 1
 
11.1%
1 1
 
11.1%
3 1
 
11.1%
5 1
 
11.1%
6 1
 
11.1%
Distinct199
Distinct (%)< 0.1%
Missing1605
Missing (%)0.3%
Memory size4.2 MiB
2025-03-04T14:25:01.276989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length13
Mean length11.35080012
Min length4

Characters and Unicode

Total characters6198706
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

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united 396005
41.3%
states 394719
41.1%
canada 29902
 
3.1%
japan 15810
 
1.6%
china 11498
 
1.2%
mexico 11426
 
1.2%
brazil 6602
 
0.7%
colombia 4387
 
0.5%
ecuador 4246
 
0.4%
cuba 4113
 
0.4%
Other values (216) 81187
 
8.5%
2025-03-04T14:25:01.369254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1202241
19.4%
e 837692
13.5%
a 633214
10.2%
n 495714
8.0%
i 481917
7.8%
d 439572
 
7.1%
413792
 
6.7%
s 411573
 
6.6%
S 401454
 
6.5%
U 396208
 
6.4%
Other values (54) 485329
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6198706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1202241
19.4%
e 837692
13.5%
a 633214
10.2%
n 495714
8.0%
i 481917
7.8%
d 439572
 
7.1%
413792
 
6.7%
s 411573
 
6.6%
S 401454
 
6.5%
U 396208
 
6.4%
Other values (54) 485329
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6198706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1202241
19.4%
e 837692
13.5%
a 633214
10.2%
n 495714
8.0%
i 481917
7.8%
d 439572
 
7.1%
413792
 
6.7%
s 411573
 
6.6%
S 401454
 
6.5%
U 396208
 
6.4%
Other values (54) 485329
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6198706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1202241
19.4%
e 837692
13.5%
a 633214
10.2%
n 495714
8.0%
i 481917
7.8%
d 439572
 
7.1%
413792
 
6.7%
s 411573
 
6.6%
S 401454
 
6.5%
U 396208
 
6.4%
Other values (54) 485329
7.8%

countryCode
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:01.399134image/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 rowWGS84
ValueCountFrequency (%)
wgs84 1
100.0%
2025-03-04T14:25:01.595404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 1
20.0%
G 1
20.0%
S 1
20.0%
8 1
20.0%
4 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 1
20.0%
G 1
20.0%
S 1
20.0%
8 1
20.0%
4 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 1
20.0%
G 1
20.0%
S 1
20.0%
8 1
20.0%
4 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 1
20.0%
G 1
20.0%
S 1
20.0%
8 1
20.0%
4 1
20.0%

stateProvince
Text

Missing 

Distinct4353
Distinct (%)0.9%
Missing47031
Missing (%)8.6%
Memory size4.2 MiB
2025-03-04T14:25:01.731702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length24
Mean length9.875326808
Min length2

Characters and Unicode

Total characters4944349
Distinct characters100
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2328 ?
Unique (%)0.5%

Sample

1st rowPennsylvania
2nd rowPennsylvania
3rd rowPennsylvania
4th rowPennsylvania
5th rowPennsylvania
ValueCountFrequency (%)
pennsylvania 190426
32.9%
virginia 18029
 
3.1%
new 16494
 
2.9%
california 14195
 
2.5%
west 12790
 
2.2%
florida 12061
 
2.1%
carolina 11784
 
2.0%
ohio 10444
 
1.8%
colorado 9885
 
1.7%
north 9412
 
1.6%
Other values (3274) 272832
47.2%
2025-03-04T14:25:01.946279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 774336
15.7%
a 709028
14.3%
i 467433
 
9.5%
e 333479
 
6.7%
s 328176
 
6.6%
l 285533
 
5.8%
o 231360
 
4.7%
y 210723
 
4.3%
P 201750
 
4.1%
v 201589
 
4.1%
Other values (90) 1200942
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4944349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 774336
15.7%
a 709028
14.3%
i 467433
 
9.5%
e 333479
 
6.7%
s 328176
 
6.6%
l 285533
 
5.8%
o 231360
 
4.7%
y 210723
 
4.3%
P 201750
 
4.1%
v 201589
 
4.1%
Other values (90) 1200942
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4944349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 774336
15.7%
a 709028
14.3%
i 467433
 
9.5%
e 333479
 
6.7%
s 328176
 
6.6%
l 285533
 
5.8%
o 231360
 
4.7%
y 210723
 
4.3%
P 201750
 
4.1%
v 201589
 
4.1%
Other values (90) 1200942
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4944349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 774336
15.7%
a 709028
14.3%
i 467433
 
9.5%
e 333479
 
6.7%
s 328176
 
6.6%
l 285533
 
5.8%
o 231360
 
4.7%
y 210723
 
4.3%
P 201750
 
4.1%
v 201589
 
4.1%
Other values (90) 1200942
24.3%

county
Text

Missing 

Distinct5726
Distinct (%)1.4%
Missing135709
Missing (%)24.8%
Memory size4.2 MiB
2025-03-04T14:25:02.097682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.634241831
Min length1

Characters and Unicode

Total characters3145300
Distinct characters100
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2471 ?
Unique (%)0.6%

Sample

1st rowLawrence
2nd rowBlair
3rd rowBedford
4th rowBeaver
5th rowArmstrong
ValueCountFrequency (%)
allegheny 22404
 
5.0%
westmoreland 18970
 
4.3%
erie 11535
 
2.6%
fayette 8996
 
2.0%
crawford 8239
 
1.8%
pref 7760
 
1.7%
butler 7581
 
1.7%
washington 7266
 
1.6%
bedford 7144
 
1.6%
somerset 6735
 
1.5%
Other values (4612) 339527
76.1%
2025-03-04T14:25:02.320096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 374970
 
11.9%
a 284458
 
9.0%
n 266261
 
8.5%
r 255493
 
8.1%
o 216431
 
6.9%
l 169266
 
5.4%
t 162803
 
5.2%
i 139637
 
4.4%
s 117314
 
3.7%
d 89876
 
2.9%
Other values (90) 1068791
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3145300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 374970
 
11.9%
a 284458
 
9.0%
n 266261
 
8.5%
r 255493
 
8.1%
o 216431
 
6.9%
l 169266
 
5.4%
t 162803
 
5.2%
i 139637
 
4.4%
s 117314
 
3.7%
d 89876
 
2.9%
Other values (90) 1068791
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3145300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 374970
 
11.9%
a 284458
 
9.0%
n 266261
 
8.5%
r 255493
 
8.1%
o 216431
 
6.9%
l 169266
 
5.4%
t 162803
 
5.2%
i 139637
 
4.4%
s 117314
 
3.7%
d 89876
 
2.9%
Other values (90) 1068791
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3145300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 374970
 
11.9%
a 284458
 
9.0%
n 266261
 
8.5%
r 255493
 
8.1%
o 216431
 
6.9%
l 169266
 
5.4%
t 162803
 
5.2%
i 139637
 
4.4%
s 117314
 
3.7%
d 89876
 
2.9%
Other values (90) 1068791
34.0%

locality
Text

Missing 

Distinct211180
Distinct (%)39.9%
Missing18209
Missing (%)3.3%
Memory size4.2 MiB
2025-03-04T14:25:02.530517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length354255
Median length206
Mean length38.29122623
Min length1

Characters and Unicode

Total characters20275166
Distinct characters166
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

Unique160172 ?
Unique (%)30.2%

Sample

1st rowElliotts Mills, along Slippery Rock Creek
2nd row(Lock Mountains), on road from Royer to Frankstown
3rd rowGravel Pit Station, Gap, 5 mi NE of Hyndman
4th row1 mi from Rt 30 crossing of Raccoon Creek
5th rownear Apollo, Roaring Run
ValueCountFrequency (%)
of 298363
 
8.3%
mi 124194
 
3.4%
along 63187
 
1.8%
creek 50001
 
1.4%
near 44899
 
1.2%
n 43877
 
1.2%
km 43778
 
1.2%
s 40926
 
1.1%
on 39763
 
1.1%
ca 37815
 
1.0%
Other values (98014) 2823379
78.2%
2025-03-04T14:25:02.780376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3063883
 
15.1%
a 1455963
 
7.2%
o 1383882
 
6.8%
e 1364716
 
6.7%
n 1094724
 
5.4%
i 1023997
 
5.1%
r 980987
 
4.8%
t 835537
 
4.1%
l 732991
 
3.6%
s 591714
 
2.9%
Other values (156) 7746772
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20275166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3063883
 
15.1%
a 1455963
 
7.2%
o 1383882
 
6.8%
e 1364716
 
6.7%
n 1094724
 
5.4%
i 1023997
 
5.1%
r 980987
 
4.8%
t 835537
 
4.1%
l 732991
 
3.6%
s 591714
 
2.9%
Other values (156) 7746772
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20275166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3063883
 
15.1%
a 1455963
 
7.2%
o 1383882
 
6.8%
e 1364716
 
6.7%
n 1094724
 
5.4%
i 1023997
 
5.1%
r 980987
 
4.8%
t 835537
 
4.1%
l 732991
 
3.6%
s 591714
 
2.9%
Other values (156) 7746772
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20275166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3063883
 
15.1%
a 1455963
 
7.2%
o 1383882
 
6.8%
e 1364716
 
6.7%
n 1094724
 
5.4%
i 1023997
 
5.1%
r 980987
 
4.8%
t 835537
 
4.1%
l 732991
 
3.6%
s 591714
 
2.9%
Other values (156) 7746772
38.2%
Distinct1997
Distinct (%)1.3%
Missing399055
Missing (%)72.9%
Memory size4.2 MiB
2025-03-04T14:25:02.939890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.231862122
Min length1

Characters and Unicode

Total characters480426
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

Unique436 ?
Unique (%)0.3%

Sample

1st row762
2nd row116
3rd row244
4th row1829
5th row2499
ValueCountFrequency (%)
300 2330
 
1.6%
250 2293
 
1.5%
400 2079
 
1.4%
350 1786
 
1.2%
100 1764
 
1.2%
335 1759
 
1.2%
200 1728
 
1.2%
305 1710
 
1.2%
1000 1586
 
1.1%
500 1567
 
1.1%
Other values (1987) 130051
87.5%
2025-03-04T14:25:03.141009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 113926
23.7%
1 62459
13.0%
2 61380
12.8%
3 51998
10.8%
5 51241
10.7%
4 35593
 
7.4%
7 28333
 
5.9%
6 27799
 
5.8%
8 25067
 
5.2%
9 22630
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 480426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 113926
23.7%
1 62459
13.0%
2 61380
12.8%
3 51998
10.8%
5 51241
10.7%
4 35593
 
7.4%
7 28333
 
5.9%
6 27799
 
5.8%
8 25067
 
5.2%
9 22630
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 480426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 113926
23.7%
1 62459
13.0%
2 61380
12.8%
3 51998
10.8%
5 51241
10.7%
4 35593
 
7.4%
7 28333
 
5.9%
6 27799
 
5.8%
8 25067
 
5.2%
9 22630
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 480426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 113926
23.7%
1 62459
13.0%
2 61380
12.8%
3 51998
10.8%
5 51241
10.7%
4 35593
 
7.4%
7 28333
 
5.9%
6 27799
 
5.8%
8 25067
 
5.2%
9 22630
 
4.7%
Distinct428
Distinct (%)2.8%
Missing532633
Missing (%)97.2%
Memory size4.2 MiB
2025-03-04T14:25:03.283518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.266069652
Min length1

Characters and Unicode

Total characters49236
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

Unique104 ?
Unique (%)0.7%

Sample

1st row2700
2nd row700
3rd row400
4th row400
5th row490
ValueCountFrequency (%)
1000 525
 
3.5%
400 517
 
3.4%
600 476
 
3.2%
300 460
 
3.1%
250 444
 
2.9%
200 441
 
2.9%
700 440
 
2.9%
800 391
 
2.6%
900 360
 
2.4%
1200 334
 
2.2%
Other values (418) 10687
70.9%
2025-03-04T14:25:03.490645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22746
46.2%
1 5599
 
11.4%
5 4808
 
9.8%
2 4636
 
9.4%
3 2993
 
6.1%
4 1975
 
4.0%
6 1945
 
4.0%
8 1728
 
3.5%
7 1639
 
3.3%
9 1167
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22746
46.2%
1 5599
 
11.4%
5 4808
 
9.8%
2 4636
 
9.4%
3 2993
 
6.1%
4 1975
 
4.0%
6 1945
 
4.0%
8 1728
 
3.5%
7 1639
 
3.3%
9 1167
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22746
46.2%
1 5599
 
11.4%
5 4808
 
9.8%
2 4636
 
9.4%
3 2993
 
6.1%
4 1975
 
4.0%
6 1945
 
4.0%
8 1728
 
3.5%
7 1639
 
3.3%
9 1167
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22746
46.2%
1 5599
 
11.4%
5 4808
 
9.8%
2 4636
 
9.4%
3 2993
 
6.1%
4 1975
 
4.0%
6 1945
 
4.0%
8 1728
 
3.5%
7 1639
 
3.3%
9 1167
 
2.4%

verbatimElevation
Text

Missing 

Distinct8691
Distinct (%)5.8%
Missing396776
Missing (%)72.4%
Memory size4.2 MiB
2025-03-04T14:25:03.633122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length15
Mean length6.798571542
Min length1

Characters and Unicode

Total characters1026122
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

Unique4182 ?
Unique (%)2.8%

Sample

1st rowca 2500 ft
2nd row380 ft
3rd row800 ft
4th row6000 ft
5th row8200 ft
ValueCountFrequency (%)
m 83502
26.5%
ft 65897
20.9%
ca 13140
 
4.2%
1000 2371
 
0.8%
300 2078
 
0.7%
400 2044
 
0.6%
250 1924
 
0.6%
350 1663
 
0.5%
800 1613
 
0.5%
900 1560
 
0.5%
Other values (5085) 139226
44.2%
2025-03-04T14:25:03.835325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 250026
24.4%
164086
16.0%
m 83889
 
8.2%
1 70399
 
6.9%
t 66088
 
6.4%
f 65827
 
6.4%
5 59639
 
5.8%
2 49786
 
4.9%
3 35411
 
3.5%
4 28634
 
2.8%
Other values (62) 152337
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1026122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 250026
24.4%
164086
16.0%
m 83889
 
8.2%
1 70399
 
6.9%
t 66088
 
6.4%
f 65827
 
6.4%
5 59639
 
5.8%
2 49786
 
4.9%
3 35411
 
3.5%
4 28634
 
2.8%
Other values (62) 152337
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1026122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 250026
24.4%
164086
16.0%
m 83889
 
8.2%
1 70399
 
6.9%
t 66088
 
6.4%
f 65827
 
6.4%
5 59639
 
5.8%
2 49786
 
4.9%
3 35411
 
3.5%
4 28634
 
2.8%
Other values (62) 152337
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1026122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 250026
24.4%
164086
16.0%
m 83889
 
8.2%
1 70399
 
6.9%
t 66088
 
6.4%
f 65827
 
6.4%
5 59639
 
5.8%
2 49786
 
4.9%
3 35411
 
3.5%
4 28634
 
2.8%
Other values (62) 152337
14.8%

verbatimDepth
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:03.867357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
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 rowBonnie L. Isaac
ValueCountFrequency (%)
bonnie 1
33.3%
l 1
33.3%
isaac 1
33.3%
2025-03-04T14:25:03.950010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2
13.3%
2
13.3%
a 2
13.3%
B 1
6.7%
o 1
6.7%
i 1
6.7%
e 1
6.7%
L 1
6.7%
. 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

maximumDistanceAboveSurfaceInMeters
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:03.978294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowcoordinates given
ValueCountFrequency (%)
coordinates 1
50.0%
given 1
50.0%
2025-03-04T14:25:04.060706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 2
11.8%
i 2
11.8%
n 2
11.8%
e 2
11.8%
c 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
a 1
 
5.9%
t 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2
11.8%
i 2
11.8%
n 2
11.8%
e 2
11.8%
c 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
a 1
 
5.9%
t 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2
11.8%
i 2
11.8%
n 2
11.8%
e 2
11.8%
c 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
a 1
 
5.9%
t 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2
11.8%
i 2
11.8%
n 2
11.8%
e 2
11.8%
c 1
 
5.9%
r 1
 
5.9%
d 1
 
5.9%
a 1
 
5.9%
t 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

locationRemarks
Text

Missing 

Distinct21
Distinct (%)14.2%
Missing547560
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:04.095566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length23
Mean length25.01351351
Min length23

Characters and Unicode

Total characters3702
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

Unique11 ?
Unique (%)7.4%

Sample

1st roworiginally had Ulster County
2nd rowlabel has Blaine County
3rd rowlabel has Blaine County
4th roworiginally had Ulster County
5th roworiginal had Jefferson County
ValueCountFrequency (%)
county 148
24.8%
label 97
16.2%
has 97
16.2%
blaine 97
16.2%
had 49
 
8.2%
originally 39
 
6.5%
original 12
 
2.0%
jefferson 12
 
2.0%
putnam 8
 
1.3%
essex 5
 
0.8%
Other values (20) 33
 
5.5%
2025-03-04T14:25:04.186523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
449
12.1%
a 419
11.3%
l 389
10.5%
n 328
 
8.9%
e 261
 
7.1%
o 216
 
5.8%
i 204
 
5.5%
y 189
 
5.1%
t 165
 
4.5%
u 159
 
4.3%
Other values (29) 923
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
449
12.1%
a 419
11.3%
l 389
10.5%
n 328
 
8.9%
e 261
 
7.1%
o 216
 
5.8%
i 204
 
5.5%
y 189
 
5.1%
t 165
 
4.5%
u 159
 
4.3%
Other values (29) 923
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
449
12.1%
a 419
11.3%
l 389
10.5%
n 328
 
8.9%
e 261
 
7.1%
o 216
 
5.8%
i 204
 
5.5%
y 189
 
5.1%
t 165
 
4.5%
u 159
 
4.3%
Other values (29) 923
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
449
12.1%
a 419
11.3%
l 389
10.5%
n 328
 
8.9%
e 261
 
7.1%
o 216
 
5.8%
i 204
 
5.5%
y 189
 
5.1%
t 165
 
4.5%
u 159
 
4.3%
Other values (29) 923
24.9%

decimalLatitude
Text

Missing 

Distinct57283
Distinct (%)18.2%
Missing232306
Missing (%)42.4%
Memory size4.2 MiB
2025-03-04T14:25:04.336689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length9
Mean length9.17869893
Min length1

Characters and Unicode

Total characters2894980
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

Unique30441 ?
Unique (%)9.7%

Sample

1st row41.032999
2nd row40.433509
3rd row39.821925
4th row40.51246
5th row40.564239
ValueCountFrequency (%)
42.163531 1766
 
0.6%
40.439124 1549
 
0.5%
39.870881 1392
 
0.4%
39.629526 898
 
0.3%
40.502052 734
 
0.2%
41.675002 625
 
0.2%
41.87621 587
 
0.2%
40.63872 538
 
0.2%
41.586789 525
 
0.2%
38.125 522
 
0.2%
Other values (56815) 306266
97.1%
2025-03-04T14:25:04.571681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 403230
13.9%
3 343451
11.9%
. 314520
10.9%
6 269872
9.3%
1 265972
9.2%
0 251632
8.7%
5 215645
7.4%
9 209638
7.2%
8 207896
7.2%
2 205730
7.1%
Other values (2) 207394
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2894980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 403230
13.9%
3 343451
11.9%
. 314520
10.9%
6 269872
9.3%
1 265972
9.2%
0 251632
8.7%
5 215645
7.4%
9 209638
7.2%
8 207896
7.2%
2 205730
7.1%
Other values (2) 207394
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2894980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 403230
13.9%
3 343451
11.9%
. 314520
10.9%
6 269872
9.3%
1 265972
9.2%
0 251632
8.7%
5 215645
7.4%
9 209638
7.2%
8 207896
7.2%
2 205730
7.1%
Other values (2) 207394
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2894980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 403230
13.9%
3 343451
11.9%
. 314520
10.9%
6 269872
9.3%
1 265972
9.2%
0 251632
8.7%
5 215645
7.4%
9 209638
7.2%
8 207896
7.2%
2 205730
7.1%
Other values (2) 207394
7.2%

decimalLongitude
Text

Missing 

Distinct56145
Distinct (%)17.8%
Missing232306
Missing (%)42.4%
Memory size4.2 MiB
2025-03-04T14:25:04.730540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length10
Mean length10.19642868
Min length1

Characters and Unicode

Total characters3215974
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

Unique29587 ?
Unique (%)9.4%

Sample

1st row-80.140462
2nd row-78.313536
3rd row-78.72617
4th row-80.366152
5th row-79.513796
ValueCountFrequency (%)
80.101233 1766
 
0.6%
79.947622 1549
 
0.5%
79.494581 1392
 
0.4%
79.955897 888
 
0.3%
80.23931 778
 
0.2%
80.423956 645
 
0.2%
79.15327 587
 
0.2%
79.75833 538
 
0.2%
72.813091 525
 
0.2%
90.675 511
 
0.2%
Other values (56033) 306223
97.1%
2025-03-04T14:25:04.967876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 391127
12.2%
8 323451
10.1%
. 314308
9.8%
- 310818
9.7%
3 279638
8.7%
6 272474
8.5%
9 270572
8.4%
1 236538
7.4%
5 207379
6.4%
0 206375
6.4%
Other values (2) 403294
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3215974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 391127
12.2%
8 323451
10.1%
. 314308
9.8%
- 310818
9.7%
3 279638
8.7%
6 272474
8.5%
9 270572
8.4%
1 236538
7.4%
5 207379
6.4%
0 206375
6.4%
Other values (2) 403294
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3215974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 391127
12.2%
8 323451
10.1%
. 314308
9.8%
- 310818
9.7%
3 279638
8.7%
6 272474
8.5%
9 270572
8.4%
1 236538
7.4%
5 207379
6.4%
0 206375
6.4%
Other values (2) 403294
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3215974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 391127
12.2%
8 323451
10.1%
. 314308
9.8%
- 310818
9.7%
3 279638
8.7%
6 272474
8.5%
9 270572
8.4%
1 236538
7.4%
5 207379
6.4%
0 206375
6.4%
Other values (2) 403294
12.5%

geodeticDatum
Text

Missing 

Distinct51
Distinct (%)< 0.1%
Missing274064
Missing (%)50.0%
Memory size4.2 MiB
2025-03-04T14:25:05.016679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.00053354
Min length3

Characters and Unicode

Total characters1368366
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

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowWGS84
2nd rowWGS84
3rd rowWGS84
4th rowWGS84
5th rowWGS84
ValueCountFrequency (%)
wgs84 237746
86.9%
nad83 35520
 
13.0%
nad27 175
 
0.1%
wgs-84 52
 
< 0.1%
nad-83 33
 
< 0.1%
nad-27 9
 
< 0.1%
nad 7
 
< 0.1%
1927 7
 
< 0.1%
gs84 6
 
< 0.1%
nad87 4
 
< 0.1%
Other values (41) 92
 
< 0.1%
2025-03-04T14:25:05.104353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 273387
20.0%
4 237818
17.4%
S 237770
17.4%
G 237769
17.4%
W 237764
17.4%
N 35835
 
2.6%
A 35835
 
2.6%
D 35835
 
2.6%
3 35561
 
2.6%
7 203
 
< 0.1%
Other values (11) 589
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1368366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 273387
20.0%
4 237818
17.4%
S 237770
17.4%
G 237769
17.4%
W 237764
17.4%
N 35835
 
2.6%
A 35835
 
2.6%
D 35835
 
2.6%
3 35561
 
2.6%
7 203
 
< 0.1%
Other values (11) 589
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1368366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 273387
20.0%
4 237818
17.4%
S 237770
17.4%
G 237769
17.4%
W 237764
17.4%
N 35835
 
2.6%
A 35835
 
2.6%
D 35835
 
2.6%
3 35561
 
2.6%
7 203
 
< 0.1%
Other values (11) 589
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1368366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 273387
20.0%
4 237818
17.4%
S 237770
17.4%
G 237769
17.4%
W 237764
17.4%
N 35835
 
2.6%
A 35835
 
2.6%
D 35835
 
2.6%
3 35561
 
2.6%
7 203
 
< 0.1%
Other values (11) 589
 
< 0.1%
Distinct9846
Distinct (%)3.6%
Missing277914
Missing (%)50.7%
Memory size4.2 MiB
2025-03-04T14:25:05.235366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length4
Mean length3.786648332
Min length1

Characters and Unicode

Total characters1021615
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

Unique2662 ?
Unique (%)1.0%

Sample

1st row250
2nd row3529
3rd row1583
4th row1500
5th row800
ValueCountFrequency (%)
3036 13977
 
5.2%
111000 12108
 
4.5%
100 11452
 
4.2%
50 7462
 
2.8%
14 7392
 
2.7%
1000 5053
 
1.9%
500 4128
 
1.5%
2000 2988
 
1.1%
4000 2948
 
1.1%
1205 2730
 
1.0%
Other values (9837) 199558
74.0%
2025-03-04T14:25:05.431355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 258581
25.3%
1 160065
15.7%
5 107966
10.6%
3 103002
 
10.1%
2 92359
 
9.0%
4 75998
 
7.4%
6 71411
 
7.0%
7 52060
 
5.1%
8 51687
 
5.1%
9 48469
 
4.7%
Other values (13) 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1021615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 258581
25.3%
1 160065
15.7%
5 107966
10.6%
3 103002
 
10.1%
2 92359
 
9.0%
4 75998
 
7.4%
6 71411
 
7.0%
7 52060
 
5.1%
8 51687
 
5.1%
9 48469
 
4.7%
Other values (13) 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1021615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 258581
25.3%
1 160065
15.7%
5 107966
10.6%
3 103002
 
10.1%
2 92359
 
9.0%
4 75998
 
7.4%
6 71411
 
7.0%
7 52060
 
5.1%
8 51687
 
5.1%
9 48469
 
4.7%
Other values (13) 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1021615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 258581
25.3%
1 160065
15.7%
5 107966
10.6%
3 103002
 
10.1%
2 92359
 
9.0%
4 75998
 
7.4%
6 71411
 
7.0%
7 52060
 
5.1%
8 51687
 
5.1%
9 48469
 
4.7%
Other values (13) 17
 
< 0.1%

verbatimCoordinates
Text

Missing 

Distinct35104
Distinct (%)7.5%
Missing81905
Missing (%)15.0%
Memory size4.2 MiB
2025-03-04T14:25:05.479923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length1
Mean length6.408133911
Min length1

Characters and Unicode

Total characters2984928
Distinct characters83
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

Unique23343 ?
Unique (%)5.0%

Sample

1st row,
2nd row,
3rd row,
4th row,
5th row,
ValueCountFrequency (%)
435646
59.6%
sec 11717
 
1.6%
nad83 4818
 
0.7%
n 3888
 
0.5%
w 3202
 
0.4%
wgs84 2066
 
0.3%
ca 2026
 
0.3%
ne 1505
 
0.2%
sw 1470
 
0.2%
wgs-84 1414
 
0.2%
Other values (40550) 262832
36.0%
2025-03-04T14:25:05.685580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 560566
18.8%
264784
 
8.9%
0 179245
 
6.0%
4 168752
 
5.7%
1 160249
 
5.4%
' 149478
 
5.0%
° 138991
 
4.7%
3 136054
 
4.6%
5 122772
 
4.1%
7 118325
 
4.0%
Other values (73) 985712
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2984928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 560566
18.8%
264784
 
8.9%
0 179245
 
6.0%
4 168752
 
5.7%
1 160249
 
5.4%
' 149478
 
5.0%
° 138991
 
4.7%
3 136054
 
4.6%
5 122772
 
4.1%
7 118325
 
4.0%
Other values (73) 985712
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2984928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 560566
18.8%
264784
 
8.9%
0 179245
 
6.0%
4 168752
 
5.7%
1 160249
 
5.4%
' 149478
 
5.0%
° 138991
 
4.7%
3 136054
 
4.6%
5 122772
 
4.1%
7 118325
 
4.0%
Other values (73) 985712
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2984928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 560566
18.8%
264784
 
8.9%
0 179245
 
6.0%
4 168752
 
5.7%
1 160249
 
5.4%
' 149478
 
5.0%
° 138991
 
4.7%
3 136054
 
4.6%
5 122772
 
4.1%
7 118325
 
4.0%
Other values (73) 985712
33.0%

verbatimCoordinateSystem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:05.712512image/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 row4235
ValueCountFrequency (%)
4235 1
100.0%
2025-03-04T14:25:05.793330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1
25.0%
2 1
25.0%
3 1
25.0%
5 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 1
25.0%
2 1
25.0%
3 1
25.0%
5 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 1
25.0%
2 1
25.0%
3 1
25.0%
5 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 1
25.0%
2 1
25.0%
3 1
25.0%
5 1
25.0%

georeferencedBy
Text

Missing 

Distinct20016
Distinct (%)8.4%
Missing310094
Missing (%)56.6%
Memory size4.2 MiB
2025-03-04T14:25:05.828229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length37
Mean length18.24573047
Min length4

Characters and Unicode

Total characters4335441
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

Unique10311 ?
Unique (%)4.3%

Sample

1st rowisaac (2018-11-12 07:39:41)
2nd rowpageshca (2018-06-20 05:52:37)
3rd rowpageshca (2020-03-23 14:49:43)
4th rowpageshca (2018-06-12 07:52:02)
5th rowpageshca (2020-03-20 09:53:43)
ValueCountFrequency (%)
isaac 107040
19.7%
david 76094
 
14.0%
newbury 76094
 
14.0%
pageshca 18937
 
3.5%
funkag 10827
 
2.0%
shannonl 8980
 
1.7%
2022-11-2022 7145
 
1.3%
l 6150
 
1.1%
bonnie 6150
 
1.1%
2018-06-06 3910
 
0.7%
Other values (17387) 222386
40.9%
2025-03-04T14:25:05.926159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 363038
 
8.4%
0 360655
 
8.3%
2 331950
 
7.7%
306099
 
7.1%
1 271958
 
6.3%
- 214184
 
4.9%
: 186880
 
4.3%
i 164858
 
3.8%
s 146397
 
3.4%
c 128998
 
3.0%
Other values (48) 1860424
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4335441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 363038
 
8.4%
0 360655
 
8.3%
2 331950
 
7.7%
306099
 
7.1%
1 271958
 
6.3%
- 214184
 
4.9%
: 186880
 
4.3%
i 164858
 
3.8%
s 146397
 
3.4%
c 128998
 
3.0%
Other values (48) 1860424
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4335441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 363038
 
8.4%
0 360655
 
8.3%
2 331950
 
7.7%
306099
 
7.1%
1 271958
 
6.3%
- 214184
 
4.9%
: 186880
 
4.3%
i 164858
 
3.8%
s 146397
 
3.4%
c 128998
 
3.0%
Other values (48) 1860424
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4335441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 363038
 
8.4%
0 360655
 
8.3%
2 331950
 
7.7%
306099
 
7.1%
1 271958
 
6.3%
- 214184
 
4.9%
: 186880
 
4.3%
i 164858
 
3.8%
s 146397
 
3.4%
c 128998
 
3.0%
Other values (48) 1860424
42.9%

georeferenceProtocol
Text

Missing 

Distinct20
Distinct (%)0.7%
Missing544682
Missing (%)99.4%
Memory size4.2 MiB
2025-03-04T14:25:05.960202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length19
Mean length22.68803701
Min length3

Characters and Unicode

Total characters68654
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

Unique6 ?
Unique (%)0.2%

Sample

1st rowGoogleMaps estimate
2nd rowGoogleMaps estimate
3rd rowGoogleMaps estimate
4th rowGoogleMaps estimate
5th rowGoogleMaps estimate
ValueCountFrequency (%)
googlemaps 1986
26.4%
estimate 1986
26.4%
given 675
 
9.0%
coordinates 675
 
9.0%
translated 633
 
8.4%
from 633
 
8.4%
vertnet 231
 
3.1%
georeferencing 231
 
3.1%
guidelines 231
 
3.1%
unknown 77
 
1.0%
Other values (35) 175
 
2.3%
2025-03-04T14:25:06.085993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9900
14.4%
t 6413
 
9.3%
o 6400
 
9.3%
a 5965
 
8.7%
s 5525
 
8.0%
4507
 
6.6%
i 4052
 
5.9%
g 3372
 
4.9%
l 2918
 
4.3%
n 2913
 
4.2%
Other values (38) 16689
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9900
14.4%
t 6413
 
9.3%
o 6400
 
9.3%
a 5965
 
8.7%
s 5525
 
8.0%
4507
 
6.6%
i 4052
 
5.9%
g 3372
 
4.9%
l 2918
 
4.3%
n 2913
 
4.2%
Other values (38) 16689
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9900
14.4%
t 6413
 
9.3%
o 6400
 
9.3%
a 5965
 
8.7%
s 5525
 
8.0%
4507
 
6.6%
i 4052
 
5.9%
g 3372
 
4.9%
l 2918
 
4.3%
n 2913
 
4.2%
Other values (38) 16689
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9900
14.4%
t 6413
 
9.3%
o 6400
 
9.3%
a 5965
 
8.7%
s 5525
 
8.0%
4507
 
6.6%
i 4052
 
5.9%
g 3372
 
4.9%
l 2918
 
4.3%
n 2913
 
4.2%
Other values (38) 16689
24.3%

georeferenceSources
Text

Missing 

Distinct1369
Distinct (%)0.6%
Missing336656
Missing (%)61.5%
Memory size4.2 MiB
2025-03-04T14:25:06.124053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length55
Mean length26.71141235
Min length3

Characters and Unicode

Total characters5637497
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

Unique149 ?
Unique (%)0.1%

Sample

1st rowgeoref batch tool 2018-11-12
2nd rowgeoref batch tool 2018-06-20; GeoLocate
3rd rowgeoref batch tool 2020-03-23
4th rowgeoref batch tool 2018-06-12
5th rowgeoref batch tool 2020-03-20
ValueCountFrequency (%)
geolocate 175379
24.0%
tool 126357
17.3%
georef 126331
17.3%
batch 126330
17.3%
from 7466
 
1.0%
label 7381
 
1.0%
dms 7375
 
1.0%
lat/long 7375
 
1.0%
2018-06-06 5119
 
0.7%
terrain 2449
 
0.3%
Other values (911) 140203
19.2%
2025-03-04T14:25:06.224417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 676091
 
12.0%
e 538868
 
9.6%
520713
 
9.2%
t 439767
 
7.8%
0 334708
 
5.9%
a 325519
 
5.8%
c 303665
 
5.4%
2 293729
 
5.2%
- 252694
 
4.5%
G 178617
 
3.2%
Other values (48) 1773126
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5637497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 676091
 
12.0%
e 538868
 
9.6%
520713
 
9.2%
t 439767
 
7.8%
0 334708
 
5.9%
a 325519
 
5.8%
c 303665
 
5.4%
2 293729
 
5.2%
- 252694
 
4.5%
G 178617
 
3.2%
Other values (48) 1773126
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5637497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 676091
 
12.0%
e 538868
 
9.6%
520713
 
9.2%
t 439767
 
7.8%
0 334708
 
5.9%
a 325519
 
5.8%
c 303665
 
5.4%
2 293729
 
5.2%
- 252694
 
4.5%
G 178617
 
3.2%
Other values (48) 1773126
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5637497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 676091
 
12.0%
e 538868
 
9.6%
520713
 
9.2%
t 439767
 
7.8%
0 334708
 
5.9%
a 325519
 
5.8%
c 303665
 
5.4%
2 293729
 
5.2%
- 252694
 
4.5%
G 178617
 
3.2%
Other values (48) 1773126
31.5%

georeferenceRemarks
Text

Missing 

Distinct3723
Distinct (%)7.6%
Missing498455
Missing (%)91.0%
Memory size4.2 MiB
2025-03-04T14:25:06.259998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length274
Median length257
Mean length38.65189938
Min length1

Characters and Unicode

Total characters1903722
Distinct characters100
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1927 ?
Unique (%)3.9%

Sample

1st row$$Westmoreland Co
2nd rowhttps://upload.wikimedia.org/wikipedia/commons/3/36/Ontario_and_Quebec_Railway_Territories_1915_map.pdf
3rd rowmapped to entire Hayes Island
4th rowmapped to Orient Bay where RR station was included all of Orient Bay (water feature)
5th rowmapped to N end of bay where collector was known to be stationed
ValueCountFrequency (%)
given 28991
 
10.4%
coordinates 28669
 
10.3%
by 23340
 
8.3%
collector 22731
 
8.1%
mapped 14982
 
5.4%
to 11994
 
4.3%
of 7296
 
2.6%
from 5824
 
2.1%
translated 4412
 
1.6%
on 2619
 
0.9%
Other values (4742) 128702
46.0%
2025-03-04T14:25:06.369802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230307
 
12.1%
o 178193
 
9.4%
e 176983
 
9.3%
t 122514
 
6.4%
n 120349
 
6.3%
r 107135
 
5.6%
a 106353
 
5.6%
i 104218
 
5.5%
c 97660
 
5.1%
l 83995
 
4.4%
Other values (90) 576015
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1903722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
230307
 
12.1%
o 178193
 
9.4%
e 176983
 
9.3%
t 122514
 
6.4%
n 120349
 
6.3%
r 107135
 
5.6%
a 106353
 
5.6%
i 104218
 
5.5%
c 97660
 
5.1%
l 83995
 
4.4%
Other values (90) 576015
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1903722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
230307
 
12.1%
o 178193
 
9.4%
e 176983
 
9.3%
t 122514
 
6.4%
n 120349
 
6.3%
r 107135
 
5.6%
a 106353
 
5.6%
i 104218
 
5.5%
c 97660
 
5.1%
l 83995
 
4.4%
Other values (90) 576015
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1903722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
230307
 
12.1%
o 178193
 
9.4%
e 176983
 
9.3%
t 122514
 
6.4%
n 120349
 
6.3%
r 107135
 
5.6%
a 106353
 
5.6%
i 104218
 
5.5%
c 97660
 
5.1%
l 83995
 
4.4%
Other values (90) 576015
30.3%

earliestEraOrLowestErathem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:06.402186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowB.L. Isaac 2023
ValueCountFrequency (%)
b.l 1
33.3%
isaac 1
33.3%
2023 1
33.3%
2025-03-04T14:25:06.482286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2
13.3%
2
13.3%
a 2
13.3%
2 2
13.3%
B 1
6.7%
L 1
6.7%
I 1
6.7%
s 1
6.7%
c 1
6.7%
0 1
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2
13.3%
2
13.3%
a 2
13.3%
2 2
13.3%
B 1
6.7%
L 1
6.7%
I 1
6.7%
s 1
6.7%
c 1
6.7%
0 1
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2
13.3%
2
13.3%
a 2
13.3%
2 2
13.3%
B 1
6.7%
L 1
6.7%
I 1
6.7%
s 1
6.7%
c 1
6.7%
0 1
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2
13.3%
2
13.3%
a 2
13.3%
2 2
13.3%
B 1
6.7%
L 1
6.7%
I 1
6.7%
s 1
6.7%
c 1
6.7%
0 1
6.7%

earliestPeriodOrLowestSystem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:06.513667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length102
Median length102
Mean length102
Min length102

Characters and Unicode

Total characters102
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPlantae|Spermatophyta|Tracheophyta|Magnoliophyta|Eudicots|Core Eudicots|Fabids|Rosids|Rosales|Rosaceae
ValueCountFrequency (%)
plantae|spermatophyta|tracheophyta|magnoliophyta|eudicots|core 1
50.0%
eudicots|fabids|rosids|rosales|rosaceae 1
50.0%
2025-03-04T14:25:06.600642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12
 
11.8%
o 10
 
9.8%
| 9
 
8.8%
s 8
 
7.8%
t 7
 
6.9%
e 7
 
6.9%
i 5
 
4.9%
c 4
 
3.9%
h 4
 
3.9%
d 4
 
3.9%
Other values (18) 32
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12
 
11.8%
o 10
 
9.8%
| 9
 
8.8%
s 8
 
7.8%
t 7
 
6.9%
e 7
 
6.9%
i 5
 
4.9%
c 4
 
3.9%
h 4
 
3.9%
d 4
 
3.9%
Other values (18) 32
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12
 
11.8%
o 10
 
9.8%
| 9
 
8.8%
s 8
 
7.8%
t 7
 
6.9%
e 7
 
6.9%
i 5
 
4.9%
c 4
 
3.9%
h 4
 
3.9%
d 4
 
3.9%
Other values (18) 32
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12
 
11.8%
o 10
 
9.8%
| 9
 
8.8%
s 8
 
7.8%
t 7
 
6.9%
e 7
 
6.9%
i 5
 
4.9%
c 4
 
3.9%
h 4
 
3.9%
d 4
 
3.9%
Other values (18) 32
31.4%

latestPeriodOrHighestSystem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:06.629784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
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 rowPlantae
ValueCountFrequency (%)
plantae 1
100.0%
2025-03-04T14:25:06.707517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

earliestEpochOrLowestSeries
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:06.736611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowMagnoliophyta
ValueCountFrequency (%)
magnoliophyta 1
100.0%
2025-03-04T14:25:06.816616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%
Distinct2
Distinct (%)100.0%
Missing547706
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:06.847088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters12
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

Unique2 ?
Unique (%)100.0%

Sample

1st rowRosales
2nd row78104
ValueCountFrequency (%)
rosales 1
50.0%
78104 1
50.0%
2025-03-04T14:25:06.943236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 2
16.7%
R 1
8.3%
o 1
8.3%
a 1
8.3%
l 1
8.3%
e 1
8.3%
7 1
8.3%
8 1
8.3%
1 1
8.3%
0 1
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2
16.7%
R 1
8.3%
o 1
8.3%
a 1
8.3%
l 1
8.3%
e 1
8.3%
7 1
8.3%
8 1
8.3%
1 1
8.3%
0 1
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2
16.7%
R 1
8.3%
o 1
8.3%
a 1
8.3%
l 1
8.3%
e 1
8.3%
7 1
8.3%
8 1
8.3%
1 1
8.3%
0 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2
16.7%
R 1
8.3%
o 1
8.3%
a 1
8.3%
l 1
8.3%
e 1
8.3%
7 1
8.3%
8 1
8.3%
1 1
8.3%
0 1
8.3%

lowestBiostratigraphicZone
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:06.970096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRosaceae
ValueCountFrequency (%)
rosaceae 1
100.0%
2025-03-04T14:25:07.050454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
25.0%
e 2
25.0%
R 1
12.5%
o 1
12.5%
s 1
12.5%
c 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
25.0%
e 2
25.0%
R 1
12.5%
o 1
12.5%
s 1
12.5%
c 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
25.0%
e 2
25.0%
R 1
12.5%
o 1
12.5%
s 1
12.5%
c 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
25.0%
e 2
25.0%
R 1
12.5%
o 1
12.5%
s 1
12.5%
c 1
12.5%

formation
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:07.078582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
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 rowRubus
ValueCountFrequency (%)
rubus 1
100.0%
2025-03-04T14:25:07.156599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 2
40.0%
R 1
20.0%
b 1
20.0%
s 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 2
40.0%
R 1
20.0%
b 1
20.0%
s 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 2
40.0%
R 1
20.0%
b 1
20.0%
s 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 2
40.0%
R 1
20.0%
b 1
20.0%
s 1
20.0%

bed
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:07.185567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
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 rowCinna arundinacea
ValueCountFrequency (%)
cinna 1
50.0%
arundinacea 1
50.0%
2025-03-04T14:25:07.267755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 4
23.5%
a 4
23.5%
i 2
11.8%
C 1
 
5.9%
1
 
5.9%
r 1
 
5.9%
u 1
 
5.9%
d 1
 
5.9%
c 1
 
5.9%
e 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 4
23.5%
a 4
23.5%
i 2
11.8%
C 1
 
5.9%
1
 
5.9%
r 1
 
5.9%
u 1
 
5.9%
d 1
 
5.9%
c 1
 
5.9%
e 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 4
23.5%
a 4
23.5%
i 2
11.8%
C 1
 
5.9%
1
 
5.9%
r 1
 
5.9%
u 1
 
5.9%
d 1
 
5.9%
c 1
 
5.9%
e 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 4
23.5%
a 4
23.5%
i 2
11.8%
C 1
 
5.9%
1
 
5.9%
r 1
 
5.9%
u 1
 
5.9%
d 1
 
5.9%
c 1
 
5.9%
e 1
 
5.9%
Distinct9
Distinct (%)0.3%
Missing544285
Missing (%)99.4%
Memory size4.2 MiB
2025-03-04T14:25:07.297728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length1
Mean length1.755769793
Min length1

Characters and Unicode

Total characters6010
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

Unique5 ?
Unique (%)0.1%

Sample

1st rowx
2nd rowx
3rd rowx
4th rowx
5th rowx
ValueCountFrequency (%)
x 2232
65.1%
cf 990
28.9%
aff 197
 
5.7%
4
 
0.1%
vel 2
 
0.1%
af 1
 
< 0.1%
sp 1
 
< 0.1%
2025-03-04T14:25:07.386467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 2232
37.1%
f 1385
23.0%
. 1189
19.8%
c 990
16.5%
a 197
 
3.3%
? 4
 
0.1%
4
 
0.1%
v 2
 
< 0.1%
e 2
 
< 0.1%
l 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
x 2232
37.1%
f 1385
23.0%
. 1189
19.8%
c 990
16.5%
a 197
 
3.3%
? 4
 
0.1%
4
 
0.1%
v 2
 
< 0.1%
e 2
 
< 0.1%
l 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
x 2232
37.1%
f 1385
23.0%
. 1189
19.8%
c 990
16.5%
a 197
 
3.3%
? 4
 
0.1%
4
 
0.1%
v 2
 
< 0.1%
e 2
 
< 0.1%
l 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
x 2232
37.1%
f 1385
23.0%
. 1189
19.8%
c 990
16.5%
a 197
 
3.3%
? 4
 
0.1%
4
 
0.1%
v 2
 
< 0.1%
e 2
 
< 0.1%
l 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

typeStatus
Text

Missing 

Distinct74
Distinct (%)2.2%
Missing544285
Missing (%)99.4%
Memory size4.2 MiB
2025-03-04T14:25:07.422604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length7
Mean length7.734151329
Min length3

Characters and Unicode

Total characters26474
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.9%

Sample

1st rowisotype
2nd rowisotype?
3rd rowisotype
4th rowisotype
5th rowisotype
ValueCountFrequency (%)
isotype 2118
54.3%
photo 463
 
11.9%
b 223
 
5.7%
isosyntype 222
 
5.7%
paratype 191
 
4.9%
syntype 138
 
3.5%
isolectotype 106
 
2.7%
type 88
 
2.3%
ti 72
 
1.8%
q 72
 
1.8%
Other values (38) 208
 
5.3%
2025-03-04T14:25:07.526639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3675
13.9%
p 3593
13.6%
t 3539
13.4%
y 3321
12.5%
e 3121
11.8%
s 2827
10.7%
i 2476
9.4%
h 518
 
2.0%
478
 
1.8%
) 445
 
1.7%
Other values (36) 2481
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3675
13.9%
p 3593
13.6%
t 3539
13.4%
y 3321
12.5%
e 3121
11.8%
s 2827
10.7%
i 2476
9.4%
h 518
 
2.0%
478
 
1.8%
) 445
 
1.7%
Other values (36) 2481
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3675
13.9%
p 3593
13.6%
t 3539
13.4%
y 3321
12.5%
e 3121
11.8%
s 2827
10.7%
i 2476
9.4%
h 518
 
2.0%
478
 
1.8%
) 445
 
1.7%
Other values (36) 2481
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3675
13.9%
p 3593
13.6%
t 3539
13.4%
y 3321
12.5%
e 3121
11.8%
s 2827
10.7%
i 2476
9.4%
h 518
 
2.0%
478
 
1.8%
) 445
 
1.7%
Other values (36) 2481
9.4%

identifiedBy
Text

Missing 

Distinct14903
Distinct (%)5.6%
Missing279934
Missing (%)51.1%
Memory size4.2 MiB
2025-03-04T14:25:07.657685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length53
Median length48
Mean length14.05411653
Min length1

Characters and Unicode

Total characters3763327
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

Unique8007 ?
Unique (%)3.0%

Sample

1st rowE.T. Wherry 1939
2nd rowE.T. Wherry
3rd rowE.T. Wherry
4th rowE.T. Wherry
5th rowE.T. Wherry 1939
ValueCountFrequency (%)
synonymy 47890
 
7.0%
isaac 35372
 
5.1%
b.l 28827
 
4.2%
cusick 9567
 
1.4%
a.w 9561
 
1.4%
1997 8383
 
1.2%
j 8168
 
1.2%
1990 7588
 
1.1%
f.h 7339
 
1.1%
utech 7337
 
1.1%
Other values (5873) 518345
75.3%
2025-03-04T14:25:07.868647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
420568
 
11.2%
. 378195
 
10.0%
n 199054
 
5.3%
9 179206
 
4.8%
y 174900
 
4.6%
a 168693
 
4.5%
1 148152
 
3.9%
s 142296
 
3.8%
o 132671
 
3.5%
e 130317
 
3.5%
Other values (101) 1689275
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3763327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
420568
 
11.2%
. 378195
 
10.0%
n 199054
 
5.3%
9 179206
 
4.8%
y 174900
 
4.6%
a 168693
 
4.5%
1 148152
 
3.9%
s 142296
 
3.8%
o 132671
 
3.5%
e 130317
 
3.5%
Other values (101) 1689275
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3763327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
420568
 
11.2%
. 378195
 
10.0%
n 199054
 
5.3%
9 179206
 
4.8%
y 174900
 
4.6%
a 168693
 
4.5%
1 148152
 
3.9%
s 142296
 
3.8%
o 132671
 
3.5%
e 130317
 
3.5%
Other values (101) 1689275
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3763327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
420568
 
11.2%
. 378195
 
10.0%
n 199054
 
5.3%
9 179206
 
4.8%
y 174900
 
4.6%
a 168693
 
4.5%
1 148152
 
3.9%
s 142296
 
3.8%
o 132671
 
3.5%
e 130317
 
3.5%
Other values (101) 1689275
44.9%

dateIdentified
Text

Missing 

Distinct4
Distinct (%)80.0%
Missing547703
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:07.910194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length90
Median length10
Mean length23.2
Min length2

Characters and Unicode

Total characters116
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

Unique3 ?
Unique (%)60.0%

Sample

1st rowL.
2nd rowPlantae|Spermatophyta|Tracheophyta|Magnoliophyta|Monocots|Commelinids|Poales|Poaceae|Cinna
3rd row1940-06-29
4th row1986
5th row1940-06-29
ValueCountFrequency (%)
1940-06-29 2
40.0%
l 1
20.0%
plantae|spermatophyta|tracheophyta|magnoliophyta|monocots|commelinids|poales|poaceae|cinna 1
20.0%
1986 1
20.0%
2025-03-04T14:25:07.993639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12
 
10.3%
o 10
 
8.6%
| 8
 
6.9%
e 7
 
6.0%
n 6
 
5.2%
t 6
 
5.2%
9 5
 
4.3%
h 4
 
3.4%
p 4
 
3.4%
l 4
 
3.4%
Other values (22) 50
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12
 
10.3%
o 10
 
8.6%
| 8
 
6.9%
e 7
 
6.0%
n 6
 
5.2%
t 6
 
5.2%
9 5
 
4.3%
h 4
 
3.4%
p 4
 
3.4%
l 4
 
3.4%
Other values (22) 50
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12
 
10.3%
o 10
 
8.6%
| 8
 
6.9%
e 7
 
6.0%
n 6
 
5.2%
t 6
 
5.2%
9 5
 
4.3%
h 4
 
3.4%
p 4
 
3.4%
l 4
 
3.4%
Other values (22) 50
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12
 
10.3%
o 10
 
8.6%
| 8
 
6.9%
e 7
 
6.0%
n 6
 
5.2%
t 6
 
5.2%
9 5
 
4.3%
h 4
 
3.4%
p 4
 
3.4%
l 4
 
3.4%
Other values (22) 50
43.1%
Distinct4
Distinct (%)80.0%
Missing547703
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:08.025298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length10
Mean length9.4
Min length4

Characters and Unicode

Total characters47
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

Unique3 ?
Unique (%)60.0%

Sample

1st rowdet. As: ?
2nd row8863
3rd rowvs. glabra [sic]
4th rowdet. As: ?
5th rowPlantae
ValueCountFrequency (%)
det 2
18.2%
as 2
18.2%
2
18.2%
8863 1
9.1%
vs 1
9.1%
glabra 1
9.1%
sic 1
9.1%
plantae 1
9.1%
2025-03-04T14:25:08.207529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
12.8%
a 4
 
8.5%
s 4
 
8.5%
t 3
 
6.4%
. 3
 
6.4%
e 3
 
6.4%
d 2
 
4.3%
8 2
 
4.3%
l 2
 
4.3%
? 2
 
4.3%
Other values (14) 16
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6
 
12.8%
a 4
 
8.5%
s 4
 
8.5%
t 3
 
6.4%
. 3
 
6.4%
e 3
 
6.4%
d 2
 
4.3%
8 2
 
4.3%
l 2
 
4.3%
? 2
 
4.3%
Other values (14) 16
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6
 
12.8%
a 4
 
8.5%
s 4
 
8.5%
t 3
 
6.4%
. 3
 
6.4%
e 3
 
6.4%
d 2
 
4.3%
8 2
 
4.3%
l 2
 
4.3%
? 2
 
4.3%
Other values (14) 16
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6
 
12.8%
a 4
 
8.5%
s 4
 
8.5%
t 3
 
6.4%
. 3
 
6.4%
e 3
 
6.4%
d 2
 
4.3%
8 2
 
4.3%
l 2
 
4.3%
? 2
 
4.3%
Other values (14) 16
34.0%

identificationVerificationStatus
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:08.236622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowMagnoliophyta
ValueCountFrequency (%)
magnoliophyta 1
100.0%
2025-03-04T14:25:08.316479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
M 1
7.7%
g 1
7.7%
n 1
7.7%
l 1
7.7%
i 1
7.7%
p 1
7.7%
h 1
7.7%
y 1
7.7%

identificationRemarks
Text

Missing 

Distinct28
Distinct (%)< 0.1%
Missing384410
Missing (%)70.2%
Memory size4.2 MiB
2025-03-04T14:25:08.348603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length16
Mean length15.90953349
Min length2

Characters and Unicode

Total characters2597995
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

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowCM filed-as name
2nd rowCM filed-as name
3rd rowCM filed-as name
4th rowCM filed-as name
5th rowCM filed-as name
ValueCountFrequency (%)
cm 162077
33.2%
name 162077
33.2%
filed-as 162077
33.2%
cf 905
 
0.2%
aff 198
 
< 0.1%
99
 
< 0.1%
det 97
 
< 0.1%
as 97
 
< 0.1%
sp 12
 
< 0.1%
vel 8
 
< 0.1%
Other values (19) 35
 
< 0.1%
2025-03-04T14:25:08.450007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
324384
12.5%
a 324367
12.5%
e 324274
12.5%
f 163382
 
6.3%
s 162199
 
6.2%
d 162182
 
6.2%
l 162098
 
6.2%
i 162096
 
6.2%
n 162090
 
6.2%
m 162082
 
6.2%
Other values (22) 488841
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2597995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
324384
12.5%
a 324367
12.5%
e 324274
12.5%
f 163382
 
6.3%
s 162199
 
6.2%
d 162182
 
6.2%
l 162098
 
6.2%
i 162096
 
6.2%
n 162090
 
6.2%
m 162082
 
6.2%
Other values (22) 488841
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2597995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
324384
12.5%
a 324367
12.5%
e 324274
12.5%
f 163382
 
6.3%
s 162199
 
6.2%
d 162182
 
6.2%
l 162098
 
6.2%
i 162096
 
6.2%
n 162090
 
6.2%
m 162082
 
6.2%
Other values (22) 488841
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2597995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
324384
12.5%
a 324367
12.5%
e 324274
12.5%
f 163382
 
6.3%
s 162199
 
6.2%
d 162182
 
6.2%
l 162098
 
6.2%
i 162096
 
6.2%
n 162090
 
6.2%
m 162082
 
6.2%
Other values (22) 488841
18.8%

taxonID
Text

Missing 

Distinct57016
Distinct (%)10.6%
Missing9329
Missing (%)1.7%
Memory size4.2 MiB
2025-03-04T14:25:08.607737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.99698911
Min length1

Characters and Unicode

Total characters2690274
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

Unique25725 ?
Unique (%)4.8%

Sample

1st row1977
2nd row1977
3rd row1977
4th row1977
5th row1977
ValueCountFrequency (%)
106714 883
 
0.2%
2452 807
 
0.1%
81829 796
 
0.1%
28139 785
 
0.1%
4170 730
 
0.1%
98731 728
 
0.1%
1977 654
 
0.1%
74186 644
 
0.1%
90989 607
 
0.1%
112585 593
 
0.1%
Other values (57006) 531152
98.7%
2025-03-04T14:25:08.831094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 417080
15.5%
8 276566
10.3%
3 268121
10.0%
7 267588
9.9%
0 263162
9.8%
9 260025
9.7%
4 254159
9.4%
2 248900
9.3%
5 219158
8.1%
6 215509
8.0%
Other values (6) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2690274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 417080
15.5%
8 276566
10.3%
3 268121
10.0%
7 267588
9.9%
0 263162
9.8%
9 260025
9.7%
4 254159
9.4%
2 248900
9.3%
5 219158
8.1%
6 215509
8.0%
Other values (6) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2690274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 417080
15.5%
8 276566
10.3%
3 268121
10.0%
7 267588
9.9%
0 263162
9.8%
9 260025
9.7%
4 254159
9.4%
2 248900
9.3%
5 219158
8.1%
6 215509
8.0%
Other values (6) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2690274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 417080
15.5%
8 276566
10.3%
3 268121
10.0%
7 267588
9.9%
0 263162
9.8%
9 260025
9.7%
4 254159
9.4%
2 248900
9.3%
5 219158
8.1%
6 215509
8.0%
Other values (6) 6
 
< 0.1%

acceptedNameUsageID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:08.879279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
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 rowPoaceae
ValueCountFrequency (%)
poaceae 1
100.0%
2025-03-04T14:25:08.961247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
28.6%
e 2
28.6%
P 1
14.3%
o 1
14.3%
c 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
28.6%
e 2
28.6%
P 1
14.3%
o 1
14.3%
c 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
28.6%
e 2
28.6%
P 1
14.3%
o 1
14.3%
c 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
28.6%
e 2
28.6%
P 1
14.3%
o 1
14.3%
c 1
14.3%

namePublishedInID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:08.987620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
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 rowCinna
ValueCountFrequency (%)
cinna 1
100.0%
2025-03-04T14:25:09.070691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2
40.0%
C 1
20.0%
i 1
20.0%
a 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2
40.0%
C 1
20.0%
i 1
20.0%
a 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2
40.0%
C 1
20.0%
i 1
20.0%
a 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2
40.0%
C 1
20.0%
i 1
20.0%
a 1
20.0%
Distinct62632
Distinct (%)11.4%
Missing660
Missing (%)0.1%
Memory size4.2 MiB
2025-03-04T14:25:09.211565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length58
Mean length19.25399234
Min length3

Characters and Unicode

Total characters10532858
Distinct characters83
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

Unique30133 ?
Unique (%)5.5%

Sample

1st rowEquisetum arvense
2nd rowEquisetum arvense
3rd rowEquisetum arvense
4th rowEquisetum arvense
5th rowEquisetum arvense
ValueCountFrequency (%)
var 31209
 
2.7%
carex 20192
 
1.7%
rubus 12663
 
1.1%
subsp 12636
 
1.1%
canadensis 9094
 
0.8%
viola 7532
 
0.6%
aster 6992
 
0.6%
solidago 6621
 
0.6%
virginiana 5124
 
0.4%
polygonum 5036
 
0.4%
Other values (28172) 1056809
90.0%
2025-03-04T14:25:09.441824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1256050
 
11.9%
i 1009412
 
9.6%
r 673323
 
6.4%
s 668488
 
6.3%
e 666844
 
6.3%
u 641419
 
6.1%
626858
 
6.0%
l 589903
 
5.6%
n 573994
 
5.4%
o 561062
 
5.3%
Other values (73) 3265505
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10532858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1256050
 
11.9%
i 1009412
 
9.6%
r 673323
 
6.4%
s 668488
 
6.3%
e 666844
 
6.3%
u 641419
 
6.1%
626858
 
6.0%
l 589903
 
5.6%
n 573994
 
5.4%
o 561062
 
5.3%
Other values (73) 3265505
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10532858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1256050
 
11.9%
i 1009412
 
9.6%
r 673323
 
6.4%
s 668488
 
6.3%
e 666844
 
6.3%
u 641419
 
6.1%
626858
 
6.0%
l 589903
 
5.6%
n 573994
 
5.4%
o 561062
 
5.3%
Other values (73) 3265505
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10532858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1256050
 
11.9%
i 1009412
 
9.6%
r 673323
 
6.4%
s 668488
 
6.3%
e 666844
 
6.3%
u 641419
 
6.1%
626858
 
6.0%
l 589903
 
5.6%
n 573994
 
5.4%
o 561062
 
5.3%
Other values (73) 3265505
31.0%

parentNameUsage
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:09.482813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters8
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 rowarundinacea
ValueCountFrequency (%)
arundinacea 1
100.0%
2025-03-04T14:25:09.560590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
27.3%
n 2
18.2%
r 1
 
9.1%
u 1
 
9.1%
d 1
 
9.1%
i 1
 
9.1%
c 1
 
9.1%
e 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
27.3%
n 2
18.2%
r 1
 
9.1%
u 1
 
9.1%
d 1
 
9.1%
i 1
 
9.1%
c 1
 
9.1%
e 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
27.3%
n 2
18.2%
r 1
 
9.1%
u 1
 
9.1%
d 1
 
9.1%
i 1
 
9.1%
c 1
 
9.1%
e 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
27.3%
n 2
18.2%
r 1
 
9.1%
u 1
 
9.1%
d 1
 
9.1%
i 1
 
9.1%
c 1
 
9.1%
e 1
 
9.1%

namePublishedIn
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing547707
Missing (%)> 99.9%
Memory size4.2 MiB
2025-03-04T14:25:09.590231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
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 rowSpecies
ValueCountFrequency (%)
species 1
100.0%
2025-03-04T14:25:09.672926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2
28.6%
S 1
14.3%
p 1
14.3%
c 1
14.3%
i 1
14.3%
s 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2
28.6%
S 1
14.3%
p 1
14.3%
c 1
14.3%
i 1
14.3%
s 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2
28.6%
S 1
14.3%
p 1
14.3%
c 1
14.3%
i 1
14.3%
s 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2
28.6%
S 1
14.3%
p 1
14.3%
c 1
14.3%
i 1
14.3%
s 1
14.3%

higherClassification
Text

Missing 

Distinct11203
Distinct (%)2.1%
Missing8875
Missing (%)1.6%
Memory size4.2 MiB
2025-03-04T14:25:09.712958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length167
Median length149
Mean length109.3439934
Min length2

Characters and Unicode

Total characters58918152
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

Unique3319 ?
Unique (%)0.6%

Sample

1st rowPlantae|Tracheophyta|Pteridophyta|Equisetopsida|Equisetales|Equisetaceae|Equisetum
2nd rowPlantae|Tracheophyta|Pteridophyta|Equisetopsida|Equisetales|Equisetaceae|Equisetum
3rd rowPlantae|Tracheophyta|Pteridophyta|Equisetopsida|Equisetales|Equisetaceae|Equisetum
4th rowPlantae|Tracheophyta|Pteridophyta|Equisetopsida|Equisetales|Equisetaceae|Equisetum
5th rowPlantae|Tracheophyta|Pteridophyta|Equisetopsida|Equisetales|Equisetaceae|Equisetum
ValueCountFrequency (%)
plantae|spermatophyta|tracheophyta|magnoliophyta|eudicots|core 357902
38.1%
plantae|spermatophyta|tracheophyta|magnoliophyta|monocots|commelinids|poales|cyperaceae|carex 18690
 
2.0%
eudicots|fabids|rosids|rosales|rosaceae|rubus 11975
 
1.3%
eudicots|fabids|rosids|malpighiales|violaceae|viola 6426
 
0.7%
eudicots|campanulids|asterids|asterales|asteraceae|aster 4980
 
0.5%
eudicots|campanulids|asterids|asterales|asteraceae|solidago 4863
 
0.5%
eudicots|caryophyllales|polygonaceae|polygonum 4401
 
0.5%
plantae|spermatophyta|tracheophyta|magnoliophyta|eudicots|ranunculales|ranunculaceae|ranunculus 4398
 
0.5%
eudicots|fabids|rosids|malpighiales|salicaceae|salix 4033
 
0.4%
plantae|spermatophyta|tracheophyta|magnoliophyta|monocots|commelinids|poales|juncaceae|juncus 3557
 
0.4%
Other values (11139) 519189
55.2%
2025-03-04T14:25:09.848541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7356723
 
12.5%
| 4835297
 
8.2%
o 4592089
 
7.8%
e 4487401
 
7.6%
t 4153122
 
7.0%
i 3168232
 
5.4%
s 3068574
 
5.2%
p 2604413
 
4.4%
l 2413723
 
4.1%
r 2398375
 
4.1%
Other values (50) 19840203
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58918152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7356723
 
12.5%
| 4835297
 
8.2%
o 4592089
 
7.8%
e 4487401
 
7.6%
t 4153122
 
7.0%
i 3168232
 
5.4%
s 3068574
 
5.2%
p 2604413
 
4.4%
l 2413723
 
4.1%
r 2398375
 
4.1%
Other values (50) 19840203
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58918152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7356723
 
12.5%
| 4835297
 
8.2%
o 4592089
 
7.8%
e 4487401
 
7.6%
t 4153122
 
7.0%
i 3168232
 
5.4%
s 3068574
 
5.2%
p 2604413
 
4.4%
l 2413723
 
4.1%
r 2398375
 
4.1%
Other values (50) 19840203
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58918152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7356723
 
12.5%
| 4835297
 
8.2%
o 4592089
 
7.8%
e 4487401
 
7.6%
t 4153122
 
7.0%
i 3168232
 
5.4%
s 3068574
 
5.2%
p 2604413
 
4.4%
l 2413723
 
4.1%
r 2398375
 
4.1%
Other values (50) 19840203
33.7%
Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size4.2 MiB
2025-03-04T14:25:09.877998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.999992697
Min length5

Characters and Unicode

Total characters3833938
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 rowPlantae
2nd rowPlantae
3rd rowPlantae
4th rowPlantae
5th rowPlantae
ValueCountFrequency (%)
plantae 547704
> 99.9%
fungi 2
 
< 0.1%
2025-03-04T14:25:09.972324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1095408
28.6%
n 547706
14.3%
P 547704
14.3%
l 547704
14.3%
t 547704
14.3%
e 547704
14.3%
F 2
 
< 0.1%
u 2
 
< 0.1%
g 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3833938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1095408
28.6%
n 547706
14.3%
P 547704
14.3%
l 547704
14.3%
t 547704
14.3%
e 547704
14.3%
F 2
 
< 0.1%
u 2
 
< 0.1%
g 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3833938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1095408
28.6%
n 547706
14.3%
P 547704
14.3%
l 547704
14.3%
t 547704
14.3%
e 547704
14.3%
F 2
 
< 0.1%
u 2
 
< 0.1%
g 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3833938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1095408
28.6%
n 547706
14.3%
P 547704
14.3%
l 547704
14.3%
t 547704
14.3%
e 547704
14.3%
F 2
 
< 0.1%
u 2
 
< 0.1%
g 2
 
< 0.1%
i 2
 
< 0.1%

phylum
Text

Missing 

Distinct11
Distinct (%)< 0.1%
Missing8876
Missing (%)1.6%
Memory size4.2 MiB
2025-03-04T14:25:10.000045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length12.95823002
Min length9

Characters and Unicode

Total characters6982309
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 (%)< 0.1%

Sample

1st rowPteridophyta
2nd rowPteridophyta
3rd rowPteridophyta
4th rowPteridophyta
5th rowPteridophyta
ValueCountFrequency (%)
magnoliophyta 503480
93.4%
pteridophyta 25737
 
4.8%
coniferophyta 5370
 
1.0%
lycopodiophyta 3942
 
0.7%
gnetophyta 141
 
< 0.1%
cycadophyta 109
 
< 0.1%
psilotophyta 35
 
< 0.1%
ginkgophyta 14
 
< 0.1%
ascomycota 2
 
< 0.1%
bryophyta 1
 
< 0.1%
2025-03-04T14:25:10.087596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1055603
15.1%
a 1042423
14.9%
t 564746
8.1%
y 542884
7.8%
p 542772
7.8%
h 538831
7.7%
i 538579
7.7%
n 509006
7.3%
l 503515
7.2%
g 503494
7.2%
Other values (15) 640456
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6982309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1055603
15.1%
a 1042423
14.9%
t 564746
8.1%
y 542884
7.8%
p 542772
7.8%
h 538831
7.7%
i 538579
7.7%
n 509006
7.3%
l 503515
7.2%
g 503494
7.2%
Other values (15) 640456
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6982309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1055603
15.1%
a 1042423
14.9%
t 564746
8.1%
y 542884
7.8%
p 542772
7.8%
h 538831
7.7%
i 538579
7.7%
n 509006
7.3%
l 503515
7.2%
g 503494
7.2%
Other values (15) 640456
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6982309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1055603
15.1%
a 1042423
14.9%
t 564746
8.1%
y 542884
7.8%
p 542772
7.8%
h 538831
7.7%
i 538579
7.7%
n 509006
7.3%
l 503515
7.2%
g 503494
7.2%
Other values (15) 640456
9.2%

class
Text

Missing 

Distinct13
Distinct (%)< 0.1%
Missing512299
Missing (%)93.5%
Memory size4.2 MiB
2025-03-04T14:25:10.118441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.0417408
Min length9

Characters and Unicode

Total characters461795
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEquisetopsida
2nd rowEquisetopsida
3rd rowEquisetopsida
4th rowEquisetopsida
5th rowEquisetopsida
ValueCountFrequency (%)
polypodiopsida 22405
63.3%
pinopsida 5370
 
15.2%
lycopodiopsida 3942
 
11.1%
equisetopsida 1782
 
5.0%
filicopsida 1497
 
4.2%
magnoliopsida 191
 
0.5%
gnetopsida 116
 
0.3%
marattiopsida 53
 
0.1%
psilotopsida 35
 
0.1%
ginkgoopsida 14
 
< 0.1%
Other values (3) 4
 
< 0.1%
2025-03-04T14:25:10.214296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 88345
19.1%
i 72194
15.6%
p 61754
13.4%
d 61754
13.4%
s 37226
8.1%
a 35708
7.7%
P 27810
 
6.0%
y 26350
 
5.7%
l 24128
 
5.2%
n 5694
 
1.2%
Other values (16) 20832
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 461795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 88345
19.1%
i 72194
15.6%
p 61754
13.4%
d 61754
13.4%
s 37226
8.1%
a 35708
7.7%
P 27810
 
6.0%
y 26350
 
5.7%
l 24128
 
5.2%
n 5694
 
1.2%
Other values (16) 20832
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 461795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 88345
19.1%
i 72194
15.6%
p 61754
13.4%
d 61754
13.4%
s 37226
8.1%
a 35708
7.7%
P 27810
 
6.0%
y 26350
 
5.7%
l 24128
 
5.2%
n 5694
 
1.2%
Other values (16) 20832
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 461795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 88345
19.1%
i 72194
15.6%
p 61754
13.4%
d 61754
13.4%
s 37226
8.1%
a 35708
7.7%
P 27810
 
6.0%
y 26350
 
5.7%
l 24128
 
5.2%
n 5694
 
1.2%
Other values (16) 20832
 
4.5%

order
Text

Missing 

Distinct93
Distinct (%)< 0.1%
Missing8877
Missing (%)1.6%
Memory size4.2 MiB
2025-03-04T14:25:10.270548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.107176462
Min length6

Characters and Unicode

Total characters4907229
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowEquisetales
2nd rowEquisetales
3rd rowEquisetales
4th rowEquisetales
5th rowEquisetales
ValueCountFrequency (%)
poales 73637
 
13.7%
asterales 62940
 
11.7%
rosales 39057
 
7.2%
lamiales 35921
 
6.7%
malpighiales 26796
 
5.0%
fabales 26049
 
4.8%
caryophyllales 24805
 
4.6%
ericales 24410
 
4.5%
polypodiales 19675
 
3.7%
ranunculales 19125
 
3.5%
Other values (83) 186416
34.6%
2025-03-04T14:25:10.389862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 823707
16.8%
s 715157
14.6%
l 705391
14.4%
e 632679
12.9%
i 268531
 
5.5%
o 213561
 
4.4%
r 184838
 
3.8%
p 122855
 
2.5%
t 111781
 
2.3%
n 110754
 
2.3%
Other values (35) 1017975
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4907229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 823707
16.8%
s 715157
14.6%
l 705391
14.4%
e 632679
12.9%
i 268531
 
5.5%
o 213561
 
4.4%
r 184838
 
3.8%
p 122855
 
2.5%
t 111781
 
2.3%
n 110754
 
2.3%
Other values (35) 1017975
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4907229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 823707
16.8%
s 715157
14.6%
l 705391
14.4%
e 632679
12.9%
i 268531
 
5.5%
o 213561
 
4.4%
r 184838
 
3.8%
p 122855
 
2.5%
t 111781
 
2.3%
n 110754
 
2.3%
Other values (35) 1017975
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4907229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 823707
16.8%
s 715157
14.6%
l 705391
14.4%
e 632679
12.9%
i 268531
 
5.5%
o 213561
 
4.4%
r 184838
 
3.8%
p 122855
 
2.5%
t 111781
 
2.3%
n 110754
 
2.3%
Other values (35) 1017975
20.7%

family
Text

Missing 

Distinct483
Distinct (%)0.1%
Missing8850
Missing (%)1.6%
Memory size4.2 MiB
2025-03-04T14:25:10.514822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.46700801
Min length7

Characters and Unicode

Total characters5640231
Distinct characters52
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

Unique46 ?
Unique (%)< 0.1%

Sample

1st rowEquisetaceae
2nd rowEquisetaceae
3rd rowEquisetaceae
4th rowEquisetaceae
5th rowEquisetaceae
ValueCountFrequency (%)
asteraceae 59036
 
11.0%
poaceae 35042
 
6.5%
rosaceae 31795
 
5.9%
cyperaceae 31104
 
5.8%
fabaceae 24067
 
4.5%
ranunculaceae 14706
 
2.7%
ericaceae 13297
 
2.5%
lamiaceae 13078
 
2.4%
brassicaceae 11612
 
2.2%
polygonaceae 8695
 
1.6%
Other values (467) 296430
55.0%
2025-03-04T14:25:10.714189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1317490
23.4%
e 1247435
22.1%
c 636926
11.3%
r 265486
 
4.7%
i 226364
 
4.0%
o 208794
 
3.7%
n 162202
 
2.9%
l 159861
 
2.8%
s 153036
 
2.7%
t 132413
 
2.3%
Other values (42) 1130224
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5640231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1317490
23.4%
e 1247435
22.1%
c 636926
11.3%
r 265486
 
4.7%
i 226364
 
4.0%
o 208794
 
3.7%
n 162202
 
2.9%
l 159861
 
2.8%
s 153036
 
2.7%
t 132413
 
2.3%
Other values (42) 1130224
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5640231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1317490
23.4%
e 1247435
22.1%
c 636926
11.3%
r 265486
 
4.7%
i 226364
 
4.0%
o 208794
 
3.7%
n 162202
 
2.9%
l 159861
 
2.8%
s 153036
 
2.7%
t 132413
 
2.3%
Other values (42) 1130224
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5640231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1317490
23.4%
e 1247435
22.1%
c 636926
11.3%
r 265486
 
4.7%
i 226364
 
4.0%
o 208794
 
3.7%
n 162202
 
2.9%
l 159861
 
2.8%
s 153036
 
2.7%
t 132413
 
2.3%
Other values (42) 1130224
20.0%

genus
Text

Missing 

Distinct7119
Distinct (%)1.3%
Missing10772
Missing (%)2.0%
Memory size4.2 MiB
2025-03-04T14:25:10.867707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length16
Mean length8.108355558
Min length3

Characters and Unicode

Total characters4353668
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1618 ?
Unique (%)0.3%

Sample

1st rowEquisetum
2nd rowEquisetum
3rd rowEquisetum
4th rowEquisetum
5th rowEquisetum
ValueCountFrequency (%)
carex 20121
 
3.7%
rubus 12541
 
2.3%
viola 7302
 
1.4%
aster 6902
 
1.3%
solidago 6574
 
1.2%
polygonum 4928
 
0.9%
ranunculus 4884
 
0.9%
salix 4325
 
0.8%
juncus 3965
 
0.7%
quercus 3394
 
0.6%
Other values (7110) 462027
86.0%
2025-03-04T14:25:11.090286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 489536
 
11.2%
i 389420
 
8.9%
e 302013
 
6.9%
u 296578
 
6.8%
r 289843
 
6.7%
o 266131
 
6.1%
s 249469
 
5.7%
l 237600
 
5.5%
n 225095
 
5.2%
m 183826
 
4.2%
Other values (44) 1424157
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4353668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 489536
 
11.2%
i 389420
 
8.9%
e 302013
 
6.9%
u 296578
 
6.8%
r 289843
 
6.7%
o 266131
 
6.1%
s 249469
 
5.7%
l 237600
 
5.5%
n 225095
 
5.2%
m 183826
 
4.2%
Other values (44) 1424157
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4353668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 489536
 
11.2%
i 389420
 
8.9%
e 302013
 
6.9%
u 296578
 
6.8%
r 289843
 
6.7%
o 266131
 
6.1%
s 249469
 
5.7%
l 237600
 
5.5%
n 225095
 
5.2%
m 183826
 
4.2%
Other values (44) 1424157
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4353668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 489536
 
11.2%
i 389420
 
8.9%
e 302013
 
6.9%
u 296578
 
6.8%
r 289843
 
6.7%
o 266131
 
6.1%
s 249469
 
5.7%
l 237600
 
5.5%
n 225095
 
5.2%
m 183826
 
4.2%
Other values (44) 1424157
32.7%

specificEpithet
Text

Missing 

Distinct17392
Distinct (%)3.3%
Missing20227
Missing (%)3.7%
Memory size4.2 MiB
2025-03-04T14:25:11.248750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.048923089
Min length3

Characters and Unicode

Total characters4773135
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

Unique5685 ?
Unique (%)1.1%

Sample

1st rowarvense
2nd rowarvense
3rd rowarvense
4th rowarvense
5th rowarvense
ValueCountFrequency (%)
canadensis 8332
 
1.6%
virginiana 4658
 
0.9%
americana 4539
 
0.9%
canadense 3020
 
0.6%
palustris 2758
 
0.5%
vulgaris 2644
 
0.5%
virginica 2622
 
0.5%
pubescens 2438
 
0.5%
repens 2076
 
0.4%
virginianum 1935
 
0.4%
Other values (17379) 492463
93.4%
2025-03-04T14:25:11.479499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 656583
13.8%
i 557783
11.7%
s 351684
 
7.4%
e 323126
 
6.8%
r 319264
 
6.7%
l 315383
 
6.6%
n 312619
 
6.5%
u 294567
 
6.2%
o 263646
 
5.5%
t 249933
 
5.2%
Other values (21) 1128547
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4773135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 656583
13.8%
i 557783
11.7%
s 351684
 
7.4%
e 323126
 
6.8%
r 319264
 
6.7%
l 315383
 
6.6%
n 312619
 
6.5%
u 294567
 
6.2%
o 263646
 
5.5%
t 249933
 
5.2%
Other values (21) 1128547
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4773135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 656583
13.8%
i 557783
11.7%
s 351684
 
7.4%
e 323126
 
6.8%
r 319264
 
6.7%
l 315383
 
6.6%
n 312619
 
6.5%
u 294567
 
6.2%
o 263646
 
5.5%
t 249933
 
5.2%
Other values (21) 1128547
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4773135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 656583
13.8%
i 557783
11.7%
s 351684
 
7.4%
e 323126
 
6.8%
r 319264
 
6.7%
l 315383
 
6.6%
n 312619
 
6.5%
u 294567
 
6.2%
o 263646
 
5.5%
t 249933
 
5.2%
Other values (21) 1128547
23.6%

infraspecificEpithet
Text

Missing 

Distinct3545
Distinct (%)8.5%
Missing505808
Missing (%)92.3%
Memory size4.2 MiB
2025-03-04T14:25:11.602614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.941980907
Min length3

Characters and Unicode

Total characters374669
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

Unique1355 ?
Unique (%)3.2%

Sample

1st rowaffine
2nd rowaffine
3rd rowaffine
4th rowaffine
5th rowaffine
ValueCountFrequency (%)
canadensis 733
 
1.7%
pubescens 705
 
1.7%
angustum 659
 
1.6%
americana 480
 
1.1%
rugosa 468
 
1.1%
virginiana 453
 
1.1%
simplex 405
 
1.0%
latiusculum 399
 
1.0%
canadense 362
 
0.9%
spectabilis 329
 
0.8%
Other values (3535) 36907
88.1%
2025-03-04T14:25:11.882083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 51935
13.9%
i 42106
11.2%
s 28853
 
7.7%
u 26699
 
7.1%
l 25235
 
6.7%
e 24586
 
6.6%
n 24301
 
6.5%
c 21124
 
5.6%
r 20928
 
5.6%
t 19267
 
5.1%
Other values (17) 89635
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 51935
13.9%
i 42106
11.2%
s 28853
 
7.7%
u 26699
 
7.1%
l 25235
 
6.7%
e 24586
 
6.6%
n 24301
 
6.5%
c 21124
 
5.6%
r 20928
 
5.6%
t 19267
 
5.1%
Other values (17) 89635
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 51935
13.9%
i 42106
11.2%
s 28853
 
7.7%
u 26699
 
7.1%
l 25235
 
6.7%
e 24586
 
6.6%
n 24301
 
6.5%
c 21124
 
5.6%
r 20928
 
5.6%
t 19267
 
5.1%
Other values (17) 89635
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 51935
13.9%
i 42106
11.2%
s 28853
 
7.7%
u 26699
 
7.1%
l 25235
 
6.7%
e 24586
 
6.6%
n 24301
 
6.5%
c 21124
 
5.6%
r 20928
 
5.6%
t 19267
 
5.1%
Other values (17) 89635
23.9%

taxonRank
Text

Missing 

Distinct8
Distinct (%)< 0.1%
Missing9330
Missing (%)1.7%
Memory size4.2 MiB
2025-03-04T14:25:11.929725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.031431448
Min length4

Characters and Unicode

Total characters3785568
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSpecies
2nd rowSpecies
3rd rowSpecies
4th rowSpecies
5th rowSpecies
ValueCountFrequency (%)
species 485579
90.2%
variety 28802
 
5.3%
subspecies 12762
 
2.4%
genus 9455
 
1.8%
family 1441
 
0.3%
form 337
 
0.1%
subform 1
 
< 0.1%
order 1
 
< 0.1%
2025-03-04T14:25:12.016340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1034940
27.3%
i 528584
14.0%
s 520558
13.8%
S 498342
13.2%
p 498341
13.2%
c 498341
13.2%
a 30243
 
0.8%
y 30243
 
0.8%
r 29142
 
0.8%
V 28802
 
0.8%
Other values (12) 88032
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3785568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1034940
27.3%
i 528584
14.0%
s 520558
13.8%
S 498342
13.2%
p 498341
13.2%
c 498341
13.2%
a 30243
 
0.8%
y 30243
 
0.8%
r 29142
 
0.8%
V 28802
 
0.8%
Other values (12) 88032
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3785568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1034940
27.3%
i 528584
14.0%
s 520558
13.8%
S 498342
13.2%
p 498341
13.2%
c 498341
13.2%
a 30243
 
0.8%
y 30243
 
0.8%
r 29142
 
0.8%
V 28802
 
0.8%
Other values (12) 88032
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3785568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1034940
27.3%
i 528584
14.0%
s 520558
13.8%
S 498342
13.2%
p 498341
13.2%
c 498341
13.2%
a 30243
 
0.8%
y 30243
 
0.8%
r 29142
 
0.8%
V 28802
 
0.8%
Other values (12) 88032
 
2.3%

verbatimTaxonRank
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing505808
Missing (%)92.3%
Memory size4.2 MiB
2025-03-04T14:25:12.045396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.548687351
Min length2

Characters and Unicode

Total characters190590
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 rowsubsp.
2nd rowsubsp.
3rd rowsubsp.
4th rowsubsp.
5th rowsubsp.
ValueCountFrequency (%)
var 29565
70.6%
subsp 11915
28.4%
f 420
 
1.0%
2025-03-04T14:25:12.139088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 41900
22.0%
v 29565
15.5%
a 29565
15.5%
r 29565
15.5%
s 23830
12.5%
u 11915
 
6.3%
b 11915
 
6.3%
p 11915
 
6.3%
f 420
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 41900
22.0%
v 29565
15.5%
a 29565
15.5%
r 29565
15.5%
s 23830
12.5%
u 11915
 
6.3%
b 11915
 
6.3%
p 11915
 
6.3%
f 420
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 41900
22.0%
v 29565
15.5%
a 29565
15.5%
r 29565
15.5%
s 23830
12.5%
u 11915
 
6.3%
b 11915
 
6.3%
p 11915
 
6.3%
f 420
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 41900
22.0%
v 29565
15.5%
a 29565
15.5%
r 29565
15.5%
s 23830
12.5%
u 11915
 
6.3%
b 11915
 
6.3%
p 11915
 
6.3%
f 420
 
0.2%
Distinct17862
Distinct (%)3.4%
Missing24606
Missing (%)4.5%
Memory size4.2 MiB
2025-03-04T14:25:12.278595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length71
Median length59
Mean length9.028617746
Min length2

Characters and Unicode

Total characters4722888
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

Unique6803 ?
Unique (%)1.3%

Sample

1st rowL.
2nd rowL.
3rd rowL.
4th rowL.
5th rowL.
ValueCountFrequency (%)
l 187821
 
19.8%
38809
 
4.1%
ex 35020
 
3.7%
michx 26635
 
2.8%
a 25087
 
2.6%
willd 20669
 
2.2%
gray 18477
 
2.0%
nutt 15076
 
1.6%
torr 12298
 
1.3%
dc 10860
 
1.1%
Other values (5628) 556601
58.8%
2025-03-04T14:25:12.514352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 646018
 
13.7%
424255
 
9.0%
e 253722
 
5.4%
L 224080
 
4.7%
l 213230
 
4.5%
r 211231
 
4.5%
a 185288
 
3.9%
n 164901
 
3.5%
i 164699
 
3.5%
) 160120
 
3.4%
Other values (100) 2075344
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4722888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 646018
 
13.7%
424255
 
9.0%
e 253722
 
5.4%
L 224080
 
4.7%
l 213230
 
4.5%
r 211231
 
4.5%
a 185288
 
3.9%
n 164901
 
3.5%
i 164699
 
3.5%
) 160120
 
3.4%
Other values (100) 2075344
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4722888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 646018
 
13.7%
424255
 
9.0%
e 253722
 
5.4%
L 224080
 
4.7%
l 213230
 
4.5%
r 211231
 
4.5%
a 185288
 
3.9%
n 164901
 
3.5%
i 164699
 
3.5%
) 160120
 
3.4%
Other values (100) 2075344
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4722888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 646018
 
13.7%
424255
 
9.0%
e 253722
 
5.4%
L 224080
 
4.7%
l 213230
 
4.5%
r 211231
 
4.5%
a 185288
 
3.9%
n 164901
 
3.5%
i 164699
 
3.5%
) 160120
 
3.4%
Other values (100) 2075344
43.9%