Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations1474154
Missing cells19497009
Missing cells (%)34.8%
Total size in memory427.4 MiB
Average record size in memory304.0 B

Variable types

Text38

Dataset

DescriptionEdinburgh (E) Herbarium Specimens 0000320-250213122211068
URLhttps://doi.org/10.15468/dl.7zm5y7

Alerts

type has constant value "PhysicalObject" Constant
institutionID has constant value "https://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799" Constant
collectionID has constant value "https://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e" Constant
institutionCode has constant value "RBGE" Constant
collectionCode has constant value "E" Constant
datasetName has constant value "Edinburgh (E) Herbarium Specimens (selected by filtering by barcode starts with E)" Constant
ownerInstitutionCode has constant value "E" Constant
basisOfRecord has constant value "HERBARIUM SHEET" Constant
informationWithheld has constant value "Sensitive location data withheld" Constant
geodeticDatum has constant value "wgs84" Constant
nomenclaturalCode has constant value "ICBN" Constant
informationWithheld has 1424405 (96.6%) missing values Missing
recordNumber has 956899 (64.9%) missing values Missing
recordedBy has 879306 (59.6%) missing values Missing
associatedMedia has 413451 (28.0%) missing values Missing
eventDate has 890415 (60.4%) missing values Missing
verbatimEventDate has 886435 (60.1%) missing values Missing
habitat has 1298143 (88.1%) missing values Missing
country has 545345 (37.0%) missing values Missing
countryCode has 545865 (37.0%) missing values Missing
stateProvince has 1041599 (70.7%) missing values Missing
county has 1379768 (93.6%) missing values Missing
locality has 1096284 (74.4%) missing values Missing
minimumElevationInMeters has 1284170 (87.1%) missing values Missing
maximumElevationInMeters has 1284170 (87.1%) missing values Missing
verbatimElevation has 1284170 (87.1%) missing values Missing
decimalLatitude has 1374815 (93.3%) missing values Missing
decimalLongitude has 1374815 (93.3%) missing values Missing
typeStatus has 1420283 (96.3%) missing values Missing
specificEpithet has 95352 (6.5%) missing values Missing
gbifID has unique values Unique
occurrenceID has unique values Unique
catalogNumber has unique values Unique

Reproduction

Analysis started2025-02-13 18:03:46.779608
Analysis finished2025-02-13 18:04:22.173181
Duration35.39 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct1474154
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:23.018410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.562302853
Min length9

Characters and Unicode

Total characters14096307
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

Unique1474154 ?
Unique (%)100.0%

Sample

1st row574854116
2nd row1913216788
3rd row575120824
4th row1913216793
5th row575159451
ValueCountFrequency (%)
574854116 1
 
< 0.1%
3312494404 1
 
< 0.1%
4522331301 1
 
< 0.1%
1913728323 1
 
< 0.1%
4522338301 1
 
< 0.1%
1913728324 1
 
< 0.1%
574861142 1
 
< 0.1%
1913728330 1
 
< 0.1%
574834855 1
 
< 0.1%
1919900052 1
 
< 0.1%
Other values (1474144) 1474144
> 99.9%
2025-02-13T13:04:24.018398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2054705
14.6%
4 1937765
13.7%
7 1544881
11.0%
3 1371816
9.7%
2 1351891
9.6%
1 1262441
9.0%
0 1249319
8.9%
9 1144600
8.1%
6 1099036
7.8%
8 1079853
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14096307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 2054705
14.6%
4 1937765
13.7%
7 1544881
11.0%
3 1371816
9.7%
2 1351891
9.6%
1 1262441
9.0%
0 1249319
8.9%
9 1144600
8.1%
6 1099036
7.8%
8 1079853
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14096307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 2054705
14.6%
4 1937765
13.7%
7 1544881
11.0%
3 1371816
9.7%
2 1351891
9.6%
1 1262441
9.0%
0 1249319
8.9%
9 1144600
8.1%
6 1099036
7.8%
8 1079853
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14096307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 2054705
14.6%
4 1937765
13.7%
7 1544881
11.0%
3 1371816
9.7%
2 1351891
9.6%
1 1262441
9.0%
0 1249319
8.9%
9 1144600
8.1%
6 1099036
7.8%
8 1079853
7.7%
Distinct415876
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:24.296141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters29483080
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

Unique391050 ?
Unique (%)26.5%

Sample

1st row2023-10-22T22:06:24Z
2nd row2023-12-01T09:31:31Z
3rd row2001-04-17T01:00:00Z
4th row2023-12-01T09:31:31Z
5th row2023-10-22T22:06:37Z
ValueCountFrequency (%)
2017-08-22t01:00:00z 2931
 
0.2%
2017-08-21t01:00:00z 2491
 
0.2%
2018-08-14t01:00:00z 2231
 
0.2%
2017-08-15t01:00:00z 2152
 
0.1%
2018-08-23t01:00:00z 2129
 
0.1%
2017-08-17t01:00:00z 2100
 
0.1%
2019-08-12t01:00:00z 2082
 
0.1%
2018-08-20t01:00:00z 2078
 
0.1%
2017-08-23t01:00:00z 2075
 
0.1%
2017-08-24t01:00:00z 2007
 
0.1%
Other values (415866) 1451878
98.5%
2025-02-13T13:04:24.580030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7420194
25.2%
2 4176333
14.2%
1 3829228
13.0%
- 2948308
 
10.0%
: 2948308
 
10.0%
T 1474154
 
5.0%
Z 1474154
 
5.0%
3 1435404
 
4.9%
4 936703
 
3.2%
5 744457
 
2.5%
Other values (4) 2095837
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29483080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7420194
25.2%
2 4176333
14.2%
1 3829228
13.0%
- 2948308
 
10.0%
: 2948308
 
10.0%
T 1474154
 
5.0%
Z 1474154
 
5.0%
3 1435404
 
4.9%
4 936703
 
3.2%
5 744457
 
2.5%
Other values (4) 2095837
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29483080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7420194
25.2%
2 4176333
14.2%
1 3829228
13.0%
- 2948308
 
10.0%
: 2948308
 
10.0%
T 1474154
 
5.0%
Z 1474154
 
5.0%
3 1435404
 
4.9%
4 936703
 
3.2%
5 744457
 
2.5%
Other values (4) 2095837
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29483080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7420194
25.2%
2 4176333
14.2%
1 3829228
13.0%
- 2948308
 
10.0%
: 2948308
 
10.0%
T 1474154
 
5.0%
Z 1474154
 
5.0%
3 1435404
 
4.9%
4 936703
 
3.2%
5 744457
 
2.5%
Other values (4) 2095837
 
7.1%

type
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:24.650437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters20638156
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhysicalObject
2nd rowPhysicalObject
3rd rowPhysicalObject
4th rowPhysicalObject
5th rowPhysicalObject
ValueCountFrequency (%)
physicalobject 1474154
100.0%
2025-02-13T13:04:24.760051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 2948308
14.3%
P 1474154
 
7.1%
h 1474154
 
7.1%
y 1474154
 
7.1%
s 1474154
 
7.1%
i 1474154
 
7.1%
a 1474154
 
7.1%
l 1474154
 
7.1%
O 1474154
 
7.1%
b 1474154
 
7.1%
Other values (3) 4422462
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20638156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2948308
14.3%
P 1474154
 
7.1%
h 1474154
 
7.1%
y 1474154
 
7.1%
s 1474154
 
7.1%
i 1474154
 
7.1%
a 1474154
 
7.1%
l 1474154
 
7.1%
O 1474154
 
7.1%
b 1474154
 
7.1%
Other values (3) 4422462
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20638156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2948308
14.3%
P 1474154
 
7.1%
h 1474154
 
7.1%
y 1474154
 
7.1%
s 1474154
 
7.1%
i 1474154
 
7.1%
a 1474154
 
7.1%
l 1474154
 
7.1%
O 1474154
 
7.1%
b 1474154
 
7.1%
Other values (3) 4422462
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20638156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2948308
14.3%
P 1474154
 
7.1%
h 1474154
 
7.1%
y 1474154
 
7.1%
s 1474154
 
7.1%
i 1474154
 
7.1%
a 1474154
 
7.1%
l 1474154
 
7.1%
O 1474154
 
7.1%
b 1474154
 
7.1%
Other values (3) 4422462
21.4%

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:24.858145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length88
Median length88
Mean length88
Min length88

Characters and Unicode

Total characters129725552
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799
2nd rowhttps://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799
3rd rowhttps://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799
4th rowhttps://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799
5th rowhttps://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799
ValueCountFrequency (%)
https://scientific-collections.gbif.org/institution/0237598a-853a-492c-af74-a723fe251799 1474154
100.0%
2025-02-13T13:04:24.998440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 11793232
 
9.1%
t 10319078
 
8.0%
c 7370770
 
5.7%
- 7370770
 
5.7%
f 5896616
 
4.5%
n 5896616
 
4.5%
7 5896616
 
4.5%
9 5896616
 
4.5%
o 5896616
 
4.5%
2 5896616
 
4.5%
Other values (19) 57492006
44.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129725552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 11793232
 
9.1%
t 10319078
 
8.0%
c 7370770
 
5.7%
- 7370770
 
5.7%
f 5896616
 
4.5%
n 5896616
 
4.5%
7 5896616
 
4.5%
9 5896616
 
4.5%
o 5896616
 
4.5%
2 5896616
 
4.5%
Other values (19) 57492006
44.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129725552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 11793232
 
9.1%
t 10319078
 
8.0%
c 7370770
 
5.7%
- 7370770
 
5.7%
f 5896616
 
4.5%
n 5896616
 
4.5%
7 5896616
 
4.5%
9 5896616
 
4.5%
o 5896616
 
4.5%
2 5896616
 
4.5%
Other values (19) 57492006
44.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129725552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 11793232
 
9.1%
t 10319078
 
8.0%
c 7370770
 
5.7%
- 7370770
 
5.7%
f 5896616
 
4.5%
n 5896616
 
4.5%
7 5896616
 
4.5%
9 5896616
 
4.5%
o 5896616
 
4.5%
2 5896616
 
4.5%
Other values (19) 57492006
44.3%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:25.090347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length87
Median length87
Mean length87
Min length87

Characters and Unicode

Total characters128251398
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e
2nd rowhttps://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e
3rd rowhttps://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e
4th rowhttps://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e
5th rowhttps://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e
ValueCountFrequency (%)
https://scientific-collections.gbif.org/collection/427c8cd7-4358-4a00-9ef3-2b2676d28d1e 1474154
100.0%
2025-02-13T13:04:25.260745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 11793232
 
9.2%
i 8844924
 
6.9%
o 7370770
 
5.7%
t 7370770
 
5.7%
- 7370770
 
5.7%
e 7370770
 
5.7%
/ 5896616
 
4.6%
l 5896616
 
4.6%
2 5896616
 
4.6%
8 4422462
 
3.4%
Other values (20) 56017852
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128251398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 11793232
 
9.2%
i 8844924
 
6.9%
o 7370770
 
5.7%
t 7370770
 
5.7%
- 7370770
 
5.7%
e 7370770
 
5.7%
/ 5896616
 
4.6%
l 5896616
 
4.6%
2 5896616
 
4.6%
8 4422462
 
3.4%
Other values (20) 56017852
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128251398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 11793232
 
9.2%
i 8844924
 
6.9%
o 7370770
 
5.7%
t 7370770
 
5.7%
- 7370770
 
5.7%
e 7370770
 
5.7%
/ 5896616
 
4.6%
l 5896616
 
4.6%
2 5896616
 
4.6%
8 4422462
 
3.4%
Other values (20) 56017852
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128251398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 11793232
 
9.2%
i 8844924
 
6.9%
o 7370770
 
5.7%
t 7370770
 
5.7%
- 7370770
 
5.7%
e 7370770
 
5.7%
/ 5896616
 
4.6%
l 5896616
 
4.6%
2 5896616
 
4.6%
8 4422462
 
3.4%
Other values (20) 56017852
43.7%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:25.336581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5896616
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRBGE
2nd rowRBGE
3rd rowRBGE
4th rowRBGE
5th rowRBGE
ValueCountFrequency (%)
rbge 1474154
100.0%
2025-02-13T13:04:25.440301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1474154
25.0%
B 1474154
25.0%
G 1474154
25.0%
E 1474154
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5896616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1474154
25.0%
B 1474154
25.0%
G 1474154
25.0%
E 1474154
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5896616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1474154
25.0%
B 1474154
25.0%
G 1474154
25.0%
E 1474154
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5896616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1474154
25.0%
B 1474154
25.0%
G 1474154
25.0%
E 1474154
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:25.473788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1474154
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE
ValueCountFrequency (%)
e 1474154
100.0%
2025-02-13T13:04:25.558094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1474154
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1474154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1474154
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1474154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1474154
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1474154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1474154
100.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:25.621761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length82
Median length82
Mean length82
Min length82

Characters and Unicode

Total characters120880628
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEdinburgh (E) Herbarium Specimens (selected by filtering by barcode starts with E)
2nd rowEdinburgh (E) Herbarium Specimens (selected by filtering by barcode starts with E)
3rd rowEdinburgh (E) Herbarium Specimens (selected by filtering by barcode starts with E)
4th rowEdinburgh (E) Herbarium Specimens (selected by filtering by barcode starts with E)
5th rowEdinburgh (E) Herbarium Specimens (selected by filtering by barcode starts with E)
ValueCountFrequency (%)
e 2948308
16.7%
by 2948308
16.7%
edinburgh 1474154
8.3%
herbarium 1474154
8.3%
specimens 1474154
8.3%
selected 1474154
8.3%
filtering 1474154
8.3%
barcode 1474154
8.3%
starts 1474154
8.3%
with 1474154
8.3%
2025-02-13T13:04:25.747855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16215694
 
13.4%
e 11793232
 
9.8%
i 8844924
 
7.3%
r 8844924
 
7.3%
t 7370770
 
6.1%
b 7370770
 
6.1%
s 5896616
 
4.9%
d 4422462
 
3.7%
c 4422462
 
3.7%
a 4422462
 
3.7%
Other values (16) 41276312
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120880628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16215694
 
13.4%
e 11793232
 
9.8%
i 8844924
 
7.3%
r 8844924
 
7.3%
t 7370770
 
6.1%
b 7370770
 
6.1%
s 5896616
 
4.9%
d 4422462
 
3.7%
c 4422462
 
3.7%
a 4422462
 
3.7%
Other values (16) 41276312
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120880628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16215694
 
13.4%
e 11793232
 
9.8%
i 8844924
 
7.3%
r 8844924
 
7.3%
t 7370770
 
6.1%
b 7370770
 
6.1%
s 5896616
 
4.9%
d 4422462
 
3.7%
c 4422462
 
3.7%
a 4422462
 
3.7%
Other values (16) 41276312
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120880628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16215694
 
13.4%
e 11793232
 
9.8%
i 8844924
 
7.3%
r 8844924
 
7.3%
t 7370770
 
6.1%
b 7370770
 
6.1%
s 5896616
 
4.9%
d 4422462
 
3.7%
c 4422462
 
3.7%
a 4422462
 
3.7%
Other values (16) 41276312
34.1%

ownerInstitutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:25.783689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1474154
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE
ValueCountFrequency (%)
e 1474154
100.0%
2025-02-13T13:04:25.867388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1474154
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1474154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1474154
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1474154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1474154
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1474154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1474154
100.0%

basisOfRecord
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:25.919914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters22112310
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHERBARIUM SHEET
2nd rowHERBARIUM SHEET
3rd rowHERBARIUM SHEET
4th rowHERBARIUM SHEET
5th rowHERBARIUM SHEET
ValueCountFrequency (%)
herbarium 1474154
50.0%
sheet 1474154
50.0%
2025-02-13T13:04:26.027090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4422462
20.0%
H 2948308
13.3%
R 2948308
13.3%
B 1474154
 
6.7%
A 1474154
 
6.7%
I 1474154
 
6.7%
U 1474154
 
6.7%
M 1474154
 
6.7%
1474154
 
6.7%
S 1474154
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22112310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4422462
20.0%
H 2948308
13.3%
R 2948308
13.3%
B 1474154
 
6.7%
A 1474154
 
6.7%
I 1474154
 
6.7%
U 1474154
 
6.7%
M 1474154
 
6.7%
1474154
 
6.7%
S 1474154
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22112310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4422462
20.0%
H 2948308
13.3%
R 2948308
13.3%
B 1474154
 
6.7%
A 1474154
 
6.7%
I 1474154
 
6.7%
U 1474154
 
6.7%
M 1474154
 
6.7%
1474154
 
6.7%
S 1474154
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22112310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4422462
20.0%
H 2948308
13.3%
R 2948308
13.3%
B 1474154
 
6.7%
A 1474154
 
6.7%
I 1474154
 
6.7%
U 1474154
 
6.7%
M 1474154
 
6.7%
1474154
 
6.7%
S 1474154
 
6.7%

informationWithheld
Text

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing1424405
Missing (%)96.6%
Memory size11.2 MiB
2025-02-13T13:04:26.086390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters1591968
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSensitive location data withheld
2nd rowSensitive location data withheld
3rd rowSensitive location data withheld
4th rowSensitive location data withheld
5th rowSensitive location data withheld
ValueCountFrequency (%)
sensitive 49749
25.0%
location 49749
25.0%
data 49749
25.0%
withheld 49749
25.0%
2025-02-13T13:04:26.193460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 198996
12.5%
t 198996
12.5%
e 149247
9.4%
149247
9.4%
a 149247
9.4%
n 99498
 
6.2%
l 99498
 
6.2%
o 99498
 
6.2%
d 99498
 
6.2%
h 99498
 
6.2%
Other values (5) 248745
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1591968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 198996
12.5%
t 198996
12.5%
e 149247
9.4%
149247
9.4%
a 149247
9.4%
n 99498
 
6.2%
l 99498
 
6.2%
o 99498
 
6.2%
d 99498
 
6.2%
h 99498
 
6.2%
Other values (5) 248745
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1591968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 198996
12.5%
t 198996
12.5%
e 149247
9.4%
149247
9.4%
a 149247
9.4%
n 99498
 
6.2%
l 99498
 
6.2%
o 99498
 
6.2%
d 99498
 
6.2%
h 99498
 
6.2%
Other values (5) 248745
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1591968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 198996
12.5%
t 198996
12.5%
e 149247
9.4%
149247
9.4%
a 149247
9.4%
n 99498
 
6.2%
l 99498
 
6.2%
o 99498
 
6.2%
d 99498
 
6.2%
h 99498
 
6.2%
Other values (5) 248745
15.6%

occurrenceID
Text

Unique 

Distinct1474154
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:26.900465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length41
Median length39
Mean length38.99996812
Min length35

Characters and Unicode

Total characters57491959
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

Unique1474154 ?
Unique (%)100.0%

Sample

1st rowhttps://data.rbge.org.uk/herb/E00135
2nd rowhttps://data.rbge.org.uk/herb/E00850129
3rd rowhttps://data.rbge.org.uk/herb/E001335
4th rowhttps://data.rbge.org.uk/herb/E00850133
5th rowhttps://data.rbge.org.uk/herb/E001515
ValueCountFrequency (%)
https://data.rbge.org.uk/herb/e00135 1
 
< 0.1%
https://data.rbge.org.uk/herb/e00850304 1
 
< 0.1%
https://data.rbge.org.uk/herb/03357:08 1
 
< 0.1%
https://data.rbge.org.uk/herb/e00850142 1
 
< 0.1%
https://data.rbge.org.uk/herb/03357:12 1
 
< 0.1%
https://data.rbge.org.uk/herb/e00850147 1
 
< 0.1%
https://data.rbge.org.uk/herb/e0013541 1
 
< 0.1%
https://data.rbge.org.uk/herb/e00850151 1
 
< 0.1%
https://data.rbge.org.uk/herb/e0013564 1
 
< 0.1%
https://data.rbge.org.uk/herb/e00850156 1
 
< 0.1%
Other values (1474144) 1474144
> 99.9%
2025-02-13T13:04:27.749885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 5896616
 
10.3%
t 4422462
 
7.7%
. 4422462
 
7.7%
r 4422462
 
7.7%
0 3385126
 
5.9%
e 2948321
 
5.1%
h 2948308
 
5.1%
a 2948308
 
5.1%
b 2948308
 
5.1%
g 2948308
 
5.1%
Other values (21) 20201278
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57491959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 5896616
 
10.3%
t 4422462
 
7.7%
. 4422462
 
7.7%
r 4422462
 
7.7%
0 3385126
 
5.9%
e 2948321
 
5.1%
h 2948308
 
5.1%
a 2948308
 
5.1%
b 2948308
 
5.1%
g 2948308
 
5.1%
Other values (21) 20201278
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57491959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 5896616
 
10.3%
t 4422462
 
7.7%
. 4422462
 
7.7%
r 4422462
 
7.7%
0 3385126
 
5.9%
e 2948321
 
5.1%
h 2948308
 
5.1%
a 2948308
 
5.1%
b 2948308
 
5.1%
g 2948308
 
5.1%
Other values (21) 20201278
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57491959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 5896616
 
10.3%
t 4422462
 
7.7%
. 4422462
 
7.7%
r 4422462
 
7.7%
0 3385126
 
5.9%
e 2948321
 
5.1%
h 2948308
 
5.1%
a 2948308
 
5.1%
b 2948308
 
5.1%
g 2948308
 
5.1%
Other values (21) 20201278
35.1%

catalogNumber
Text

Unique 

Distinct1474154
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:28.490973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.999968117
Min length5

Characters and Unicode

Total characters13267339
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

Unique1474154 ?
Unique (%)100.0%

Sample

1st rowE00135
2nd rowE00850129
3rd rowE001335
4th rowE00850133
5th rowE001515
ValueCountFrequency (%)
e00135 1
 
< 0.1%
e00850304 1
 
< 0.1%
03357:08 1
 
< 0.1%
e00850142 1
 
< 0.1%
03357:12 1
 
< 0.1%
e00850147 1
 
< 0.1%
e0013541 1
 
< 0.1%
e00850151 1
 
< 0.1%
e0013564 1
 
< 0.1%
e00850156 1
 
< 0.1%
Other values (1474144) 1474144
> 99.9%
2025-02-13T13:04:29.194514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3385126
25.5%
E 1474133
11.1%
1 1442648
10.9%
3 930505
 
7.0%
4 929539
 
7.0%
2 928562
 
7.0%
5 846719
 
6.4%
9 835142
 
6.3%
6 832509
 
6.3%
8 831743
 
6.3%
Other values (7) 830713
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13267339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3385126
25.5%
E 1474133
11.1%
1 1442648
10.9%
3 930505
 
7.0%
4 929539
 
7.0%
2 928562
 
7.0%
5 846719
 
6.4%
9 835142
 
6.3%
6 832509
 
6.3%
8 831743
 
6.3%
Other values (7) 830713
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13267339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3385126
25.5%
E 1474133
11.1%
1 1442648
10.9%
3 930505
 
7.0%
4 929539
 
7.0%
2 928562
 
7.0%
5 846719
 
6.4%
9 835142
 
6.3%
6 832509
 
6.3%
8 831743
 
6.3%
Other values (7) 830713
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13267339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3385126
25.5%
E 1474133
11.1%
1 1442648
10.9%
3 930505
 
7.0%
4 929539
 
7.0%
2 928562
 
7.0%
5 846719
 
6.4%
9 835142
 
6.3%
6 832509
 
6.3%
8 831743
 
6.3%
Other values (7) 830713
 
6.3%

recordNumber
Text

Missing 

Distinct149837
Distinct (%)29.0%
Missing956899
Missing (%)64.9%
Memory size11.2 MiB
2025-02-13T13:04:29.394217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length38
Mean length4.395373655
Min length1

Characters and Unicode

Total characters2273529
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109622 ?
Unique (%)21.2%

Sample

1st row206
2nd row4840
3rd row1312
4th row5207
5th row30902
ValueCountFrequency (%)
wat 6624
 
1.2%
s.n 2034
 
0.4%
sn 1504
 
0.3%
lao 1353
 
0.2%
d 1270
 
0.2%
mjr 1179
 
0.2%
w 787
 
0.1%
rsnb 671
 
0.1%
2 658
 
0.1%
1 657
 
0.1%
Other values (131595) 526551
96.9%
2025-02-13T13:04:29.648458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 309421
13.6%
2 262407
11.5%
3 212492
9.3%
4 198147
8.7%
5 192640
8.5%
0 188055
8.3%
6 179024
7.9%
9 173209
7.6%
8 173012
7.6%
7 171132
7.5%
Other values (74) 213990
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2273529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 309421
13.6%
2 262407
11.5%
3 212492
9.3%
4 198147
8.7%
5 192640
8.5%
0 188055
8.3%
6 179024
7.9%
9 173209
7.6%
8 173012
7.6%
7 171132
7.5%
Other values (74) 213990
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2273529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 309421
13.6%
2 262407
11.5%
3 212492
9.3%
4 198147
8.7%
5 192640
8.5%
0 188055
8.3%
6 179024
7.9%
9 173209
7.6%
8 173012
7.6%
7 171132
7.5%
Other values (74) 213990
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2273529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 309421
13.6%
2 262407
11.5%
3 212492
9.3%
4 198147
8.7%
5 192640
8.5%
0 188055
8.3%
6 179024
7.9%
9 173209
7.6%
8 173012
7.6%
7 171132
7.5%
Other values (74) 213990
9.4%

recordedBy
Text

Missing 

Distinct16627
Distinct (%)2.8%
Missing879306
Missing (%)59.6%
Memory size11.2 MiB
2025-02-13T13:04:29.780815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length258
Median length187
Mean length27.87366521
Min length4

Characters and Unicode

Total characters16580594
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5786 ?
Unique (%)1.0%

Sample

1st rowHarvey, William Henry
2nd rowStainton, John David Adam, Sykes, William Russell & Williams, Leonard Howard John
3rd rowSino-American Botanical Expedition (1984),
4th rowStainton, John David Adam, Sykes, William Russell & Williams, Leonard Howard John
5th rowLong, David Geoffrey
ValueCountFrequency (%)
136970
 
5.4%
john 49067
 
2.0%
expedition 43259
 
1.7%
david 36240
 
1.4%
peter 34964
 
1.4%
george 34894
 
1.4%
m 30576
 
1.2%
j 30240
 
1.2%
davis 28252
 
1.1%
hadland 27649
 
1.1%
Other values (14301) 2061764
82.0%
2025-02-13T13:04:29.985665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1922412
 
11.6%
e 1217589
 
7.3%
a 1152435
 
7.0%
n 991075
 
6.0%
, 966180
 
5.8%
r 962130
 
5.8%
i 940354
 
5.7%
o 816309
 
4.9%
l 610421
 
3.7%
t 517606
 
3.1%
Other values (106) 6484083
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16580594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1922412
 
11.6%
e 1217589
 
7.3%
a 1152435
 
7.0%
n 991075
 
6.0%
, 966180
 
5.8%
r 962130
 
5.8%
i 940354
 
5.7%
o 816309
 
4.9%
l 610421
 
3.7%
t 517606
 
3.1%
Other values (106) 6484083
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16580594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1922412
 
11.6%
e 1217589
 
7.3%
a 1152435
 
7.0%
n 991075
 
6.0%
, 966180
 
5.8%
r 962130
 
5.8%
i 940354
 
5.7%
o 816309
 
4.9%
l 610421
 
3.7%
t 517606
 
3.1%
Other values (106) 6484083
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16580594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1922412
 
11.6%
e 1217589
 
7.3%
a 1152435
 
7.0%
n 991075
 
6.0%
, 966180
 
5.8%
r 962130
 
5.8%
i 940354
 
5.7%
o 816309
 
4.9%
l 610421
 
3.7%
t 517606
 
3.1%
Other values (106) 6484083
39.1%
Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:30.055283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length15
Mean length15.29975837
Min length15

Characters and Unicode

Total characters22554200
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

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowHERBARIUM SHEET
2nd rowHERBARIUM SHEET
3rd rowHERBARIUM SHEET
4th rowHERBARIUM SHEET
5th rowHERBARIUM SHEET
ValueCountFrequency (%)
herbarium 1474154
49.6%
sheet 1465974
49.3%
sheet|herbarium 21673
 
0.7%
sheet|silica-dried 3608
 
0.1%
sheet|spirit 3493
 
0.1%
sheet|carpological 665
 
< 0.1%
sheet|spirit|herbarium 227
 
< 0.1%
sheet|photographic 190
 
< 0.1%
specimen 158
 
< 0.1%
sheet|microscope 92
 
< 0.1%
Other values (30) 357
 
< 0.1%
2025-02-13T13:04:30.180657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4422467
19.6%
H 2970266
13.2%
R 2948309
13.1%
1496437
 
6.6%
S 1481702
 
6.6%
M 1474246
 
6.5%
A 1474155
 
6.5%
I 1474154
 
6.5%
U 1474154
 
6.5%
B 1474154
 
6.5%
Other values (28) 1864156
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22554200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4422467
19.6%
H 2970266
13.2%
R 2948309
13.1%
1496437
 
6.6%
S 1481702
 
6.6%
M 1474246
 
6.5%
A 1474155
 
6.5%
I 1474154
 
6.5%
U 1474154
 
6.5%
B 1474154
 
6.5%
Other values (28) 1864156
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22554200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4422467
19.6%
H 2970266
13.2%
R 2948309
13.1%
1496437
 
6.6%
S 1481702
 
6.6%
M 1474246
 
6.5%
A 1474155
 
6.5%
I 1474154
 
6.5%
U 1474154
 
6.5%
B 1474154
 
6.5%
Other values (28) 1864156
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22554200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4422467
19.6%
H 2970266
13.2%
R 2948309
13.1%
1496437
 
6.6%
S 1481702
 
6.6%
M 1474246
 
6.5%
A 1474155
 
6.5%
I 1474154
 
6.5%
U 1474154
 
6.5%
B 1474154
 
6.5%
Other values (28) 1864156
8.3%

associatedMedia
Text

Missing 

Distinct1060703
Distinct (%)100.0%
Missing413451
Missing (%)28.0%
Memory size11.2 MiB
2025-02-13T13:04:30.667764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length652
Median length68
Mean length68.32677762
Min length68

Characters and Unicode

Total characters72474418
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1060703 ?
Unique (%)100.0%

Sample

1st rowhttps://iiif.rbge.org.uk/herb/iiif/E00850138/full/300,/0/default.jpg
2nd rowhttps://iiif.rbge.org.uk/herb/iiif/E00850142/full/300,/0/default.jpg
3rd rowhttps://iiif.rbge.org.uk/herb/iiif/E00850165/full/300,/0/default.jpg
4th rowhttps://iiif.rbge.org.uk/herb/iiif/E00850174/full/300,/0/default.jpg
5th rowhttps://iiif.rbge.org.uk/herb/iiif/E00000002/full/300,/0/default.jpg
ValueCountFrequency (%)
4748
 
0.4%
full/300,/0/default.jpg 10
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00259028/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239650/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239574/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239565/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239420/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239627/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239582/full/300,/0/default.jpg 2
 
< 0.1%
https://iiif.rbge.org.uk/herb/iiif/e00239560/full/300,/0/default.jpg 2
 
< 0.1%
Other values (1065316) 1065436
99.6%
2025-02-13T13:04:31.182876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 9589059
 
13.2%
i 6392706
 
8.8%
0 5588223
 
7.7%
f 4261810
 
5.9%
. 4261805
 
5.9%
e 3196722
 
4.4%
g 3196356
 
4.4%
l 3196354
 
4.4%
u 3196353
 
4.4%
t 3196353
 
4.4%
Other values (31) 26398677
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72474418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 9589059
 
13.2%
i 6392706
 
8.8%
0 5588223
 
7.7%
f 4261810
 
5.9%
. 4261805
 
5.9%
e 3196722
 
4.4%
g 3196356
 
4.4%
l 3196354
 
4.4%
u 3196353
 
4.4%
t 3196353
 
4.4%
Other values (31) 26398677
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72474418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 9589059
 
13.2%
i 6392706
 
8.8%
0 5588223
 
7.7%
f 4261810
 
5.9%
. 4261805
 
5.9%
e 3196722
 
4.4%
g 3196356
 
4.4%
l 3196354
 
4.4%
u 3196353
 
4.4%
t 3196353
 
4.4%
Other values (31) 26398677
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72474418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 9589059
 
13.2%
i 6392706
 
8.8%
0 5588223
 
7.7%
f 4261810
 
5.9%
. 4261805
 
5.9%
e 3196722
 
4.4%
g 3196356
 
4.4%
l 3196354
 
4.4%
u 3196353
 
4.4%
t 3196353
 
4.4%
Other values (31) 26398677
36.4%

eventDate
Text

Missing 

Distinct50412
Distinct (%)8.6%
Missing890415
Missing (%)60.4%
Memory size11.2 MiB
2025-02-13T13:04:31.313030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.290552798
Min length4

Characters and Unicode

Total characters5423258
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

Unique10662 ?
Unique (%)1.8%

Sample

1st row1954-04-17
2nd row1984-07-27
3rd row1954-05-04
4th row2002-02-01
5th row1899-01-14
ValueCountFrequency (%)
1802 2301
 
0.4%
1837 822
 
0.1%
1831 718
 
0.1%
1896-01 630
 
0.1%
1908 615
 
0.1%
1898 590
 
0.1%
1863 588
 
0.1%
1896 582
 
0.1%
1835 581
 
0.1%
1913 579
 
0.1%
Other values (50402) 575733
98.6%
2025-02-13T13:04:31.498741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1029434
19.0%
0 958892
17.7%
1 924535
17.0%
9 651141
12.0%
2 450835
8.3%
8 319130
 
5.9%
7 254740
 
4.7%
6 246579
 
4.5%
5 216221
 
4.0%
3 187817
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5423258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1029434
19.0%
0 958892
17.7%
1 924535
17.0%
9 651141
12.0%
2 450835
8.3%
8 319130
 
5.9%
7 254740
 
4.7%
6 246579
 
4.5%
5 216221
 
4.0%
3 187817
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5423258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1029434
19.0%
0 958892
17.7%
1 924535
17.0%
9 651141
12.0%
2 450835
8.3%
8 319130
 
5.9%
7 254740
 
4.7%
6 246579
 
4.5%
5 216221
 
4.0%
3 187817
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5423258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1029434
19.0%
0 958892
17.7%
1 924535
17.0%
9 651141
12.0%
2 450835
8.3%
8 319130
 
5.9%
7 254740
 
4.7%
6 246579
 
4.5%
5 216221
 
4.0%
3 187817
 
3.5%

verbatimEventDate
Text

Missing 

Distinct51743
Distinct (%)8.8%
Missing886435
Missing (%)60.1%
Memory size11.2 MiB
2025-02-13T13:04:31.679153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length42
Mean length14.03413706
Min length2

Characters and Unicode

Total characters8248129
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11666 ?
Unique (%)2.0%

Sample

1st row17th April 1954
2nd row27th July 1984
3rd row4th May 1954
4th row1st February 2002
5th row14th January 1899
ValueCountFrequency (%)
july 80580
 
5.0%
august 68396
 
4.2%
june 67491
 
4.2%
may 66131
 
4.1%
september 52067
 
3.2%
april 50330
 
3.1%
october 41027
 
2.5%
march 38693
 
2.4%
february 26321
 
1.6%
november 24218
 
1.5%
Other values (1069) 1105058
68.2%
2025-02-13T13:04:31.926695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1032594
 
12.5%
1 804548
 
9.8%
9 601521
 
7.3%
t 579547
 
7.0%
h 416779
 
5.1%
2 410164
 
5.0%
e 386970
 
4.7%
u 331300
 
4.0%
r 325950
 
4.0%
0 313191
 
3.8%
Other values (67) 3045565
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8248129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1032594
 
12.5%
1 804548
 
9.8%
9 601521
 
7.3%
t 579547
 
7.0%
h 416779
 
5.1%
2 410164
 
5.0%
e 386970
 
4.7%
u 331300
 
4.0%
r 325950
 
4.0%
0 313191
 
3.8%
Other values (67) 3045565
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8248129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1032594
 
12.5%
1 804548
 
9.8%
9 601521
 
7.3%
t 579547
 
7.0%
h 416779
 
5.1%
2 410164
 
5.0%
e 386970
 
4.7%
u 331300
 
4.0%
r 325950
 
4.0%
0 313191
 
3.8%
Other values (67) 3045565
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8248129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1032594
 
12.5%
1 804548
 
9.8%
9 601521
 
7.3%
t 579547
 
7.0%
h 416779
 
5.1%
2 410164
 
5.0%
e 386970
 
4.7%
u 331300
 
4.0%
r 325950
 
4.0%
0 313191
 
3.8%
Other values (67) 3045565
36.9%

habitat
Text

Missing 

Distinct95847
Distinct (%)54.5%
Missing1298143
Missing (%)88.1%
Memory size11.2 MiB
2025-02-13T13:04:32.084251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2730
Median length848
Mean length51.55765265
Min length1

Characters and Unicode

Total characters9074714
Distinct characters146
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75755 ?
Unique (%)43.0%

Sample

1st rowGully in shady Quercus forest; on shady boulder
2nd rowOpen scrubby pine forest on river bank; on boulder
3rd rowOn steep cliff banks in open broad leaved forest.
4th rowSmall pocket wet and shady ground, north facing under small shrubs.; Vegetation: Cotoneaster and Rose
5th rowStream banks on lower south slopes
ValueCountFrequency (%)
forest 50886
 
3.8%
on 48422
 
3.7%
in 48339
 
3.6%
and 29970
 
2.3%
of 29826
 
2.3%
with 24147
 
1.8%
vegetation 22771
 
1.7%
by 16876
 
1.3%
evergreen 16135
 
1.2%
growing 15052
 
1.1%
Other values (30420) 1022828
77.2%
2025-02-13T13:04:32.309112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1186419
13.1%
e 822196
 
9.1%
a 642558
 
7.1%
o 636771
 
7.0%
r 549485
 
6.1%
n 534442
 
5.9%
s 515247
 
5.7%
i 493956
 
5.4%
t 457307
 
5.0%
l 349402
 
3.9%
Other values (136) 2886931
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9074714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1186419
13.1%
e 822196
 
9.1%
a 642558
 
7.1%
o 636771
 
7.0%
r 549485
 
6.1%
n 534442
 
5.9%
s 515247
 
5.7%
i 493956
 
5.4%
t 457307
 
5.0%
l 349402
 
3.9%
Other values (136) 2886931
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9074714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1186419
13.1%
e 822196
 
9.1%
a 642558
 
7.1%
o 636771
 
7.0%
r 549485
 
6.1%
n 534442
 
5.9%
s 515247
 
5.7%
i 493956
 
5.4%
t 457307
 
5.0%
l 349402
 
3.9%
Other values (136) 2886931
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9074714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1186419
13.1%
e 822196
 
9.1%
a 642558
 
7.1%
o 636771
 
7.0%
r 549485
 
6.1%
n 534442
 
5.9%
s 515247
 
5.7%
i 493956
 
5.4%
t 457307
 
5.0%
l 349402
 
3.9%
Other values (136) 2886931
31.8%
Distinct37
Distinct (%)< 0.1%
Missing3484
Missing (%)0.2%
Memory size11.2 MiB
2025-02-13T13:04:32.401562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length19.59909633
Min length5

Characters and Unicode

Total characters28823803
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthern Africa
2nd rowNepal
3rd rowInner China, Korea and Taiwan
4th rowNepal
5th rowIndia, Bangladesh & Pakistan
ValueCountFrequency (%)
and 718393
 
15.7%
britain 419714
 
9.2%
ireland 419714
 
9.2%
america 185965
 
4.1%
asia 170410
 
3.7%
excl 157254
 
3.4%
europe 157254
 
3.4%
china 155605
 
3.4%
egypt 154573
 
3.4%
west 154573
 
3.4%
Other values (52) 1893229
41.3%
2025-02-13T13:04:32.545897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3776873
13.1%
3116014
 
10.8%
n 2822158
 
9.8%
i 2204391
 
7.6%
r 2042901
 
7.1%
e 1868893
 
6.5%
d 1418108
 
4.9%
t 1299457
 
4.5%
l 1091665
 
3.8%
I 710981
 
2.5%
Other values (41) 8472362
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28823803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3776873
13.1%
3116014
 
10.8%
n 2822158
 
9.8%
i 2204391
 
7.6%
r 2042901
 
7.1%
e 1868893
 
6.5%
d 1418108
 
4.9%
t 1299457
 
4.5%
l 1091665
 
3.8%
I 710981
 
2.5%
Other values (41) 8472362
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28823803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3776873
13.1%
3116014
 
10.8%
n 2822158
 
9.8%
i 2204391
 
7.6%
r 2042901
 
7.1%
e 1868893
 
6.5%
d 1418108
 
4.9%
t 1299457
 
4.5%
l 1091665
 
3.8%
I 710981
 
2.5%
Other values (41) 8472362
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28823803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3776873
13.1%
3116014
 
10.8%
n 2822158
 
9.8%
i 2204391
 
7.6%
r 2042901
 
7.1%
e 1868893
 
6.5%
d 1418108
 
4.9%
t 1299457
 
4.5%
l 1091665
 
3.8%
I 710981
 
2.5%
Other values (41) 8472362
29.4%

country
Text

Missing 

Distinct237
Distinct (%)< 0.1%
Missing545345
Missing (%)37.0%
Memory size11.2 MiB
2025-02-13T13:04:32.685451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length44
Median length36
Mean length8.858823504
Min length3

Characters and Unicode

Total characters8228155
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

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowSouth Africa
2nd rowNepal
3rd rowChina
4th rowNepal
5th rowIndia
ValueCountFrequency (%)
united 266174
21.1%
kingdom 242506
19.2%
china 90544
 
7.2%
turkey 62633
 
5.0%
nepal 46019
 
3.6%
australia 38726
 
3.1%
india 32547
 
2.6%
myanmar 22488
 
1.8%
states 22389
 
1.8%
iran 19776
 
1.6%
Other values (269) 420132
33.2%
2025-02-13T13:04:32.888647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 921908
 
11.2%
n 893281
 
10.9%
a 777470
 
9.4%
d 613877
 
7.5%
e 590560
 
7.2%
t 425806
 
5.2%
o 349910
 
4.3%
335125
 
4.1%
m 312614
 
3.8%
r 285933
 
3.5%
Other values (50) 2721671
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8228155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 921908
 
11.2%
n 893281
 
10.9%
a 777470
 
9.4%
d 613877
 
7.5%
e 590560
 
7.2%
t 425806
 
5.2%
o 349910
 
4.3%
335125
 
4.1%
m 312614
 
3.8%
r 285933
 
3.5%
Other values (50) 2721671
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8228155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 921908
 
11.2%
n 893281
 
10.9%
a 777470
 
9.4%
d 613877
 
7.5%
e 590560
 
7.2%
t 425806
 
5.2%
o 349910
 
4.3%
335125
 
4.1%
m 312614
 
3.8%
r 285933
 
3.5%
Other values (50) 2721671
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8228155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 921908
 
11.2%
n 893281
 
10.9%
a 777470
 
9.4%
d 613877
 
7.5%
e 590560
 
7.2%
t 425806
 
5.2%
o 349910
 
4.3%
335125
 
4.1%
m 312614
 
3.8%
r 285933
 
3.5%
Other values (50) 2721671
33.1%

countryCode
Text

Missing 

Distinct227
Distinct (%)< 0.1%
Missing545865
Missing (%)37.0%
Memory size11.2 MiB
2025-02-13T13:04:33.049587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.007967346
Min length2

Characters and Unicode

Total characters1863974
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

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowZA
2nd rowNP
3rd rowCN
4th rowNP
5th rowIN
ValueCountFrequency (%)
gb 242506
26.1%
cn 90544
 
9.8%
tr 62633
 
6.7%
np 46019
 
5.0%
au 38726
 
4.2%
in 32547
 
3.5%
mm 22488
 
2.4%
us 22367
 
2.4%
ir 19776
 
2.1%
br 16852
 
1.8%
Other values (217) 333831
36.0%
2025-02-13T13:04:33.340781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 284326
15.3%
G 278496
14.9%
N 180580
9.7%
C 129772
 
7.0%
R 120963
 
6.5%
T 101691
 
5.5%
A 99119
 
5.3%
M 97468
 
5.2%
I 87169
 
4.7%
P 85254
 
4.6%
Other values (16) 399136
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1863974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 284326
15.3%
G 278496
14.9%
N 180580
9.7%
C 129772
 
7.0%
R 120963
 
6.5%
T 101691
 
5.5%
A 99119
 
5.3%
M 97468
 
5.2%
I 87169
 
4.7%
P 85254
 
4.6%
Other values (16) 399136
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1863974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 284326
15.3%
G 278496
14.9%
N 180580
9.7%
C 129772
 
7.0%
R 120963
 
6.5%
T 101691
 
5.5%
A 99119
 
5.3%
M 97468
 
5.2%
I 87169
 
4.7%
P 85254
 
4.6%
Other values (16) 399136
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1863974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 284326
15.3%
G 278496
14.9%
N 180580
9.7%
C 129772
 
7.0%
R 120963
 
6.5%
T 101691
 
5.5%
A 99119
 
5.3%
M 97468
 
5.2%
I 87169
 
4.7%
P 85254
 
4.6%
Other values (16) 399136
21.4%

stateProvince
Text

Missing 

Distinct1855
Distinct (%)0.4%
Missing1041599
Missing (%)70.7%
Memory size11.2 MiB
2025-02-13T13:04:33.517040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length54
Median length50
Mean length7.96349366
Min length3

Characters and Unicode

Total characters3444649
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique311 ?
Unique (%)0.1%

Sample

1st rowScotland
2nd rowGuangdong
3rd rowSouss - Massa - Draâ
4th rowChiang Rai
5th rowWestern Cape
ValueCountFrequency (%)
scotland 144901
29.4%
england 66519
 
13.5%
yunnan 17739
 
3.6%
wales 8031
 
1.6%
ireland 6401
 
1.3%
of 5046
 
1.0%
republic 4998
 
1.0%
xizang 4024
 
0.8%
sarawak 3406
 
0.7%
sichuan 3288
 
0.7%
Other values (2037) 227876
46.3%
2025-02-13T13:04:33.759285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 507588
14.7%
n 454262
13.2%
l 287691
 
8.4%
d 247278
 
7.2%
o 220716
 
6.4%
t 200819
 
5.8%
S 174699
 
5.1%
c 171828
 
5.0%
i 111048
 
3.2%
g 103418
 
3.0%
Other values (124) 965302
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3444649
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 507588
14.7%
n 454262
13.2%
l 287691
 
8.4%
d 247278
 
7.2%
o 220716
 
6.4%
t 200819
 
5.8%
S 174699
 
5.1%
c 171828
 
5.0%
i 111048
 
3.2%
g 103418
 
3.0%
Other values (124) 965302
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3444649
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 507588
14.7%
n 454262
13.2%
l 287691
 
8.4%
d 247278
 
7.2%
o 220716
 
6.4%
t 200819
 
5.8%
S 174699
 
5.1%
c 171828
 
5.0%
i 111048
 
3.2%
g 103418
 
3.0%
Other values (124) 965302
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3444649
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 507588
14.7%
n 454262
13.2%
l 287691
 
8.4%
d 247278
 
7.2%
o 220716
 
6.4%
t 200819
 
5.8%
S 174699
 
5.1%
c 171828
 
5.0%
i 111048
 
3.2%
g 103418
 
3.0%
Other values (124) 965302
28.0%

county
Text

Missing 

Distinct966
Distinct (%)1.0%
Missing1379768
Missing (%)93.6%
Memory size11.2 MiB
2025-02-13T13:04:33.895381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length23
Mean length13.80088149
Min length3

Characters and Unicode

Total characters1302610
Distinct characters114
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)0.3%

Sample

1st rowShantou
2nd rowAgadir-Ida ou Tanane
3rd rowDêqên Tibetan
4th rowDêqên Tibetan
5th rowDêqên Tibetan
ValueCountFrequency (%)
west 6871
 
3.5%
north 6192
 
3.2%
vc83 4865
 
2.5%
midlothian 4865
 
2.5%
mid 4325
 
2.2%
east 4266
 
2.2%
perthshire 4101
 
2.1%
south 3802
 
1.9%
ebudes 3743
 
1.9%
vc88 3415
 
1.7%
Other values (1192) 149812
76.3%
2025-02-13T13:04:34.094840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101872
 
7.8%
e 89725
 
6.9%
i 80379
 
6.2%
a 78101
 
6.0%
r 75564
 
5.8%
C 69908
 
5.4%
V 63309
 
4.9%
t 61694
 
4.7%
s 59792
 
4.6%
h 56453
 
4.3%
Other values (104) 565813
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1302610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
101872
 
7.8%
e 89725
 
6.9%
i 80379
 
6.2%
a 78101
 
6.0%
r 75564
 
5.8%
C 69908
 
5.4%
V 63309
 
4.9%
t 61694
 
4.7%
s 59792
 
4.6%
h 56453
 
4.3%
Other values (104) 565813
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1302610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
101872
 
7.8%
e 89725
 
6.9%
i 80379
 
6.2%
a 78101
 
6.0%
r 75564
 
5.8%
C 69908
 
5.4%
V 63309
 
4.9%
t 61694
 
4.7%
s 59792
 
4.6%
h 56453
 
4.3%
Other values (104) 565813
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1302610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
101872
 
7.8%
e 89725
 
6.9%
i 80379
 
6.2%
a 78101
 
6.0%
r 75564
 
5.8%
C 69908
 
5.4%
V 63309
 
4.9%
t 61694
 
4.7%
s 59792
 
4.6%
h 56453
 
4.3%
Other values (104) 565813
43.4%

locality
Text

Missing 

Distinct197733
Distinct (%)52.3%
Missing1096284
Missing (%)74.4%
Memory size11.2 MiB
2025-02-13T13:04:34.350257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length844
Median length329
Mean length56.77751872
Min length1

Characters and Unicode

Total characters21454521
Distinct characters183
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154409 ?
Unique (%)40.9%

Sample

1st rowNepal:Hills north of Pokhara
2nd rowNepal:Majhkot, Madi Khola
3rd rowIndia:Uttarakhand:Nainital District:path from Nainital-Khurpatal road to Land’s End
4th rowViti Levu
5th rowChina:Yunnan:Zhongdian (Shangrila) County:River valley in Bi Ta Hai Forest reserve
ValueCountFrequency (%)
of 121239
 
4.6%
united 57310
 
2.2%
the 44747
 
1.7%
kingdom:scotland:(vc 36862
 
1.4%
to 33607
 
1.3%
km 33163
 
1.3%
de 30970
 
1.2%
from 23954
 
0.9%
road 23510
 
0.9%
on 21335
 
0.8%
Other values (180508) 2202286
83.8%
2025-02-13T13:04:34.650985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2255942
 
10.5%
a 2018517
 
9.4%
n 1447336
 
6.7%
e 1348189
 
6.3%
i 1228477
 
5.7%
o 1133449
 
5.3%
r 998573
 
4.7%
t 809527
 
3.8%
: 768818
 
3.6%
l 712958
 
3.3%
Other values (173) 8732735
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21454521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2255942
 
10.5%
a 2018517
 
9.4%
n 1447336
 
6.7%
e 1348189
 
6.3%
i 1228477
 
5.7%
o 1133449
 
5.3%
r 998573
 
4.7%
t 809527
 
3.8%
: 768818
 
3.6%
l 712958
 
3.3%
Other values (173) 8732735
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21454521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2255942
 
10.5%
a 2018517
 
9.4%
n 1447336
 
6.7%
e 1348189
 
6.3%
i 1228477
 
5.7%
o 1133449
 
5.3%
r 998573
 
4.7%
t 809527
 
3.8%
: 768818
 
3.6%
l 712958
 
3.3%
Other values (173) 8732735
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21454521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2255942
 
10.5%
a 2018517
 
9.4%
n 1447336
 
6.7%
e 1348189
 
6.3%
i 1228477
 
5.7%
o 1133449
 
5.3%
r 998573
 
4.7%
t 809527
 
3.8%
: 768818
 
3.6%
l 712958
 
3.3%
Other values (173) 8732735
40.7%
Distinct3592
Distinct (%)1.9%
Missing1284170
Missing (%)87.1%
Memory size11.2 MiB
2025-02-13T13:04:34.831938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.46284424
Min length1

Characters and Unicode

Total characters657885
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

Unique592 ?
Unique (%)0.3%

Sample

1st row1676
2nd row610
3rd row2090
4th row3360
5th row1500
ValueCountFrequency (%)
1000 3101
 
1.6%
800 2831
 
1.5%
2000 2702
 
1.4%
100 2632
 
1.4%
1200 2506
 
1.3%
1500 2236
 
1.2%
500 2233
 
1.2%
1300 2102
 
1.1%
600 2095
 
1.1%
200 2092
 
1.1%
Other values (3568) 165454
87.1%
2025-02-13T13:04:35.072252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 657885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 657885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 657885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%
Distinct3592
Distinct (%)1.9%
Missing1284170
Missing (%)87.1%
Memory size11.2 MiB
2025-02-13T13:04:35.253026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.46284424
Min length1

Characters and Unicode

Total characters657885
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

Unique592 ?
Unique (%)0.3%

Sample

1st row1676
2nd row610
3rd row2090
4th row3360
5th row1500
ValueCountFrequency (%)
1000 3101
 
1.6%
800 2831
 
1.5%
2000 2702
 
1.4%
100 2632
 
1.4%
1200 2506
 
1.3%
1500 2236
 
1.2%
500 2233
 
1.2%
1300 2102
 
1.1%
600 2095
 
1.1%
200 2092
 
1.1%
Other values (3568) 165454
87.1%
2025-02-13T13:04:35.500542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 657885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 657885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 657885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 203623
31.0%
1 96414
14.7%
2 76265
 
11.6%
5 62876
 
9.6%
3 54180
 
8.2%
4 38234
 
5.8%
8 34084
 
5.2%
6 33063
 
5.0%
7 32451
 
4.9%
9 26626
 
4.0%

verbatimElevation
Text

Missing 

Distinct3592
Distinct (%)1.9%
Missing1284170
Missing (%)87.1%
Memory size11.2 MiB
2025-02-13T13:04:35.669711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.46284424
Min length2

Characters and Unicode

Total characters847869
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

Unique592 ?
Unique (%)0.3%

Sample

1st row1676m
2nd row610m
3rd row2090m
4th row3360m
5th row1500m
ValueCountFrequency (%)
1000m 3101
 
1.6%
800m 2831
 
1.5%
2000m 2702
 
1.4%
100m 2632
 
1.4%
1200m 2506
 
1.3%
1500m 2236
 
1.2%
500m 2233
 
1.2%
1300m 2102
 
1.1%
600m 2095
 
1.1%
200m 2092
 
1.1%
Other values (3568) 165454
87.1%
2025-02-13T13:04:35.895721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 203623
24.0%
m 189984
22.4%
1 96414
11.4%
2 76265
 
9.0%
5 62876
 
7.4%
3 54180
 
6.4%
4 38234
 
4.5%
8 34084
 
4.0%
6 33063
 
3.9%
7 32451
 
3.8%
Other values (2) 26695
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 847869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 203623
24.0%
m 189984
22.4%
1 96414
11.4%
2 76265
 
9.0%
5 62876
 
7.4%
3 54180
 
6.4%
4 38234
 
4.5%
8 34084
 
4.0%
6 33063
 
3.9%
7 32451
 
3.8%
Other values (2) 26695
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 847869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 203623
24.0%
m 189984
22.4%
1 96414
11.4%
2 76265
 
9.0%
5 62876
 
7.4%
3 54180
 
6.4%
4 38234
 
4.5%
8 34084
 
4.0%
6 33063
 
3.9%
7 32451
 
3.8%
Other values (2) 26695
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 847869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 203623
24.0%
m 189984
22.4%
1 96414
11.4%
2 76265
 
9.0%
5 62876
 
7.4%
3 54180
 
6.4%
4 38234
 
4.5%
8 34084
 
4.0%
6 33063
 
3.9%
7 32451
 
3.8%
Other values (2) 26695
 
3.1%

decimalLatitude
Text

Missing 

Distinct20146
Distinct (%)20.3%
Missing1374815
Missing (%)93.3%
Memory size11.2 MiB
2025-02-13T13:04:36.032068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.051903079
Min length4

Characters and Unicode

Total characters899207
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

Unique8736 ?
Unique (%)8.8%

Sample

1st row29.381944
2nd row31.883333
3rd row28.000000
4th row27.500000
5th row35.400000
ValueCountFrequency (%)
16.868611 410
 
0.4%
27.750000 373
 
0.4%
28.666667 235
 
0.2%
2.783333 230
 
0.2%
27.500000 219
 
0.2%
16.733333 217
 
0.2%
25.500000 201
 
0.2%
25.666667 174
 
0.2%
27.801389 173
 
0.2%
27.700000 170
 
0.2%
Other values (19019) 96937
97.6%
2025-02-13T13:04:36.224633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 117482
13.1%
3 112081
12.5%
. 99339
11.0%
6 98309
10.9%
2 88463
9.8%
7 78085
8.7%
1 75267
8.4%
8 61457
6.8%
5 60081
6.7%
4 48899
5.4%
Other values (2) 59744
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 117482
13.1%
3 112081
12.5%
. 99339
11.0%
6 98309
10.9%
2 88463
9.8%
7 78085
8.7%
1 75267
8.4%
8 61457
6.8%
5 60081
6.7%
4 48899
5.4%
Other values (2) 59744
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 117482
13.1%
3 112081
12.5%
. 99339
11.0%
6 98309
10.9%
2 88463
9.8%
7 78085
8.7%
1 75267
8.4%
8 61457
6.8%
5 60081
6.7%
4 48899
5.4%
Other values (2) 59744
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 117482
13.1%
3 112081
12.5%
. 99339
11.0%
6 98309
10.9%
2 88463
9.8%
7 78085
8.7%
1 75267
8.4%
8 61457
6.8%
5 60081
6.7%
4 48899
5.4%
Other values (2) 59744
6.6%

decimalLongitude
Text

Missing 

Distinct21422
Distinct (%)21.6%
Missing1374815
Missing (%)93.3%
Memory size11.2 MiB
2025-02-13T13:04:36.365414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.392846717
Min length2

Characters and Unicode

Total characters933076
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

Unique9802 ?
Unique (%)9.9%

Sample

1st row79.442778
2nd row-116.050000
3rd row100.750000
4th row100.166667
5th row46.050000
ValueCountFrequency (%)
89.050556 411
 
0.4%
98.800000 376
 
0.4%
98.500000 341
 
0.3%
87.500000 286
 
0.3%
98.966667 275
 
0.3%
98.250000 232
 
0.2%
88.983333 207
 
0.2%
98.616667 201
 
0.2%
56.250000 198
 
0.2%
98.566667 183
 
0.2%
Other values (20705) 96629
97.3%
2025-02-13T13:04:36.561739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 129283
13.9%
3 102925
11.0%
. 99338
10.6%
6 98870
10.6%
8 87576
9.4%
1 81207
8.7%
7 75916
8.1%
9 65204
7.0%
5 62657
6.7%
4 52307
5.6%
Other values (2) 77793
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 933076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 129283
13.9%
3 102925
11.0%
. 99338
10.6%
6 98870
10.6%
8 87576
9.4%
1 81207
8.7%
7 75916
8.1%
9 65204
7.0%
5 62657
6.7%
4 52307
5.6%
Other values (2) 77793
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 933076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 129283
13.9%
3 102925
11.0%
. 99338
10.6%
6 98870
10.6%
8 87576
9.4%
1 81207
8.7%
7 75916
8.1%
9 65204
7.0%
5 62657
6.7%
4 52307
5.6%
Other values (2) 77793
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 933076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 129283
13.9%
3 102925
11.0%
. 99338
10.6%
6 98870
10.6%
8 87576
9.4%
1 81207
8.7%
7 75916
8.1%
9 65204
7.0%
5 62657
6.7%
4 52307
5.6%
Other values (2) 77793
8.3%

geodeticDatum
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:36.621099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7370770
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwgs84
2nd rowwgs84
3rd rowwgs84
4th rowwgs84
5th rowwgs84
ValueCountFrequency (%)
wgs84 1474154
100.0%
2025-02-13T13:04:36.720087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 1474154
20.0%
g 1474154
20.0%
s 1474154
20.0%
8 1474154
20.0%
4 1474154
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7370770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 1474154
20.0%
g 1474154
20.0%
s 1474154
20.0%
8 1474154
20.0%
4 1474154
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7370770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 1474154
20.0%
g 1474154
20.0%
s 1474154
20.0%
8 1474154
20.0%
4 1474154
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7370770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 1474154
20.0%
g 1474154
20.0%
s 1474154
20.0%
8 1474154
20.0%
4 1474154
20.0%

typeStatus
Text

Missing 

Distinct43226
Distinct (%)80.2%
Missing1420283
Missing (%)96.3%
Memory size11.2 MiB
2025-02-13T13:04:36.859912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length269
Median length197
Mean length42.57672959
Min length4

Characters and Unicode

Total characters2293651
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36011 ?
Unique (%)66.8%

Sample

1st rowIsotype: Heracleum bhutanicum M.F.Watson
2nd rowPossible Type: Hydrocotyle tripartita R.Br. ex Rich.
3rd rowSyntype: Hydrocotyle siamica Craib. | Isotype: Hydrocotyle siamensis H. Wolff
4th rowType: Hydrocotyle polycephala Wight & Arn.
5th rowType: Centella dentata Adamson
ValueCountFrequency (%)
isotype 19757
 
7.2%
type 15811
 
5.7%
12664
 
4.6%
syntype 7434
 
2.7%
holotype 5826
 
2.1%
isosyntype 4338
 
1.6%
ex 4227
 
1.5%
possible 3071
 
1.1%
arn 2365
 
0.9%
hook 2281
 
0.8%
Other values (33259) 198412
71.8%
2025-02-13T13:04:37.078353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
222517
 
9.7%
e 179918
 
7.8%
a 161424
 
7.0%
i 139202
 
6.1%
o 138049
 
6.0%
s 121415
 
5.3%
t 109326
 
4.8%
r 106771
 
4.7%
n 102562
 
4.5%
l 97080
 
4.2%
Other values (87) 915387
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2293651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
222517
 
9.7%
e 179918
 
7.8%
a 161424
 
7.0%
i 139202
 
6.1%
o 138049
 
6.0%
s 121415
 
5.3%
t 109326
 
4.8%
r 106771
 
4.7%
n 102562
 
4.5%
l 97080
 
4.2%
Other values (87) 915387
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2293651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
222517
 
9.7%
e 179918
 
7.8%
a 161424
 
7.0%
i 139202
 
6.1%
o 138049
 
6.0%
s 121415
 
5.3%
t 109326
 
4.8%
r 106771
 
4.7%
n 102562
 
4.5%
l 97080
 
4.2%
Other values (87) 915387
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2293651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
222517
 
9.7%
e 179918
 
7.8%
a 161424
 
7.0%
i 139202
 
6.1%
o 138049
 
6.0%
s 121415
 
5.3%
t 109326
 
4.8%
r 106771
 
4.7%
n 102562
 
4.5%
l 97080
 
4.2%
Other values (87) 915387
39.9%
Distinct165287
Distinct (%)11.2%
Missing1758
Missing (%)0.1%
Memory size11.2 MiB
2025-02-13T13:04:37.243552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length99
Median length84
Mean length29.39825156
Min length4

Characters and Unicode

Total characters43285868
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58709 ?
Unique (%)4.0%

Sample

1st rowHarveya capensis Hook.
2nd rowMaytenus thomsonii (Kurz) Raju & Babu
3rd rowStrobilanthes claviculata C.B.Clarke ex W.W.Sm.
4th rowReissantia arborea (Roxb.) Hara
5th rowPorella L.
ValueCountFrequency (%)
l 360607
 
6.6%
156004
 
2.9%
ex 96370
 
1.8%
dc 45614
 
0.8%
boiss 31559
 
0.6%
benth 27540
 
0.5%
wall 25288
 
0.5%
rhododendron 23038
 
0.4%
hook.f 22266
 
0.4%
carex 21942
 
0.4%
Other values (85244) 4625012
85.1%
2025-02-13T13:04:37.482874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3966442
 
9.2%
a 3860358
 
8.9%
i 3142581
 
7.3%
e 2651462
 
6.1%
r 2376666
 
5.5%
s 2149648
 
5.0%
o 2133272
 
4.9%
l 2110974
 
4.9%
. 2036773
 
4.7%
n 1988600
 
4.6%
Other values (124) 16869092
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43285868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3966442
 
9.2%
a 3860358
 
8.9%
i 3142581
 
7.3%
e 2651462
 
6.1%
r 2376666
 
5.5%
s 2149648
 
5.0%
o 2133272
 
4.9%
l 2110974
 
4.9%
. 2036773
 
4.7%
n 1988600
 
4.6%
Other values (124) 16869092
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43285868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3966442
 
9.2%
a 3860358
 
8.9%
i 3142581
 
7.3%
e 2651462
 
6.1%
r 2376666
 
5.5%
s 2149648
 
5.0%
o 2133272
 
4.9%
l 2110974
 
4.9%
. 2036773
 
4.7%
n 1988600
 
4.6%
Other values (124) 16869092
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43285868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3966442
 
9.2%
a 3860358
 
8.9%
i 3142581
 
7.3%
e 2651462
 
6.1%
r 2376666
 
5.5%
s 2149648
 
5.0%
o 2133272
 
4.9%
l 2110974
 
4.9%
. 2036773
 
4.7%
n 1988600
 
4.6%
Other values (124) 16869092
39.0%

family
Text

Distinct1165
Distinct (%)0.1%
Missing4415
Missing (%)0.3%
Memory size11.2 MiB
2025-02-13T13:04:37.598800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length11.20208554
Min length6

Characters and Unicode

Total characters16464142
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)< 0.1%

Sample

1st rowOrobanchaceae
2nd rowCelastraceae
3rd rowAcanthaceae
4th rowCelastraceae
5th rowPorellaceae
ValueCountFrequency (%)
compositae 156233
 
10.6%
leguminosae 65166
 
4.4%
labiatae 62695
 
4.3%
gramineae 44155
 
3.0%
ericaceae 39173
 
2.7%
umbelliferae 35351
 
2.4%
rosaceae 33347
 
2.3%
ranunculaceae 33324
 
2.3%
cyperaceae 32899
 
2.2%
orchidaceae 30282
 
2.1%
Other values (1155) 937114
63.8%
2025-02-13T13:04:37.786313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3333350
20.2%
e 3113653
18.9%
c 1429551
 
8.7%
i 1073000
 
6.5%
o 854449
 
5.2%
r 755620
 
4.6%
n 620864
 
3.8%
l 545853
 
3.3%
t 497013
 
3.0%
m 433240
 
2.6%
Other values (45) 3807549
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16464142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3333350
20.2%
e 3113653
18.9%
c 1429551
 
8.7%
i 1073000
 
6.5%
o 854449
 
5.2%
r 755620
 
4.6%
n 620864
 
3.8%
l 545853
 
3.3%
t 497013
 
3.0%
m 433240
 
2.6%
Other values (45) 3807549
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16464142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3333350
20.2%
e 3113653
18.9%
c 1429551
 
8.7%
i 1073000
 
6.5%
o 854449
 
5.2%
r 755620
 
4.6%
n 620864
 
3.8%
l 545853
 
3.3%
t 497013
 
3.0%
m 433240
 
2.6%
Other values (45) 3807549
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16464142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3333350
20.2%
e 3113653
18.9%
c 1429551
 
8.7%
i 1073000
 
6.5%
o 854449
 
5.2%
r 755620
 
4.6%
n 620864
 
3.8%
l 545853
 
3.3%
t 497013
 
3.0%
m 433240
 
2.6%
Other values (45) 3807549
23.1%

genus
Text

Distinct14376
Distinct (%)1.0%
Missing11662
Missing (%)0.8%
Memory size11.2 MiB
2025-02-13T13:04:37.926387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length18
Mean length8.56612549
Min length2

Characters and Unicode

Total characters12527890
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2403 ?
Unique (%)0.2%

Sample

1st rowHarveya
2nd rowMaytenus
3rd rowStrobilanthes
4th rowReissantia
5th rowPorella
ValueCountFrequency (%)
rhododendron 23008
 
1.6%
carex 21942
 
1.5%
salix 11628
 
0.8%
primula 11183
 
0.8%
saxifraga 10323
 
0.7%
ranunculus 10251
 
0.7%
hieracium 10235
 
0.7%
euphorbia 9945
 
0.7%
juncus 8086
 
0.6%
senecio 7523
 
0.5%
Other values (14368) 1338478
91.5%
2025-02-13T13:04:38.132840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1472990
 
11.8%
i 1147371
 
9.2%
e 886367
 
7.1%
r 854570
 
6.8%
o 836221
 
6.7%
u 728010
 
5.8%
l 681604
 
5.4%
n 665797
 
5.3%
s 661284
 
5.3%
m 513182
 
4.1%
Other values (46) 4080494
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12527890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1472990
 
11.8%
i 1147371
 
9.2%
e 886367
 
7.1%
r 854570
 
6.8%
o 836221
 
6.7%
u 728010
 
5.8%
l 681604
 
5.4%
n 665797
 
5.3%
s 661284
 
5.3%
m 513182
 
4.1%
Other values (46) 4080494
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12527890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1472990
 
11.8%
i 1147371
 
9.2%
e 886367
 
7.1%
r 854570
 
6.8%
o 836221
 
6.7%
u 728010
 
5.8%
l 681604
 
5.4%
n 665797
 
5.3%
s 661284
 
5.3%
m 513182
 
4.1%
Other values (46) 4080494
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12527890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1472990
 
11.8%
i 1147371
 
9.2%
e 886367
 
7.1%
r 854570
 
6.8%
o 836221
 
6.7%
u 728010
 
5.8%
l 681604
 
5.4%
n 665797
 
5.3%
s 661284
 
5.3%
m 513182
 
4.1%
Other values (46) 4080494
32.6%

specificEpithet
Text

Missing 

Distinct48475
Distinct (%)3.5%
Missing95352
Missing (%)6.5%
Memory size11.2 MiB
2025-02-13T13:04:38.274629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length67
Median length41
Mean length9.094293452
Min length1

Characters and Unicode

Total characters12539230
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

Unique13923 ?
Unique (%)1.0%

Sample

1st rowcapensis
2nd rowthomsonii
3rd rowclaviculata
4th rowarborea
5th rowpaniculatum
ValueCountFrequency (%)
x 6687
 
0.5%
× 5477
 
0.4%
vulgaris 5054
 
0.4%
arvensis 4864
 
0.3%
alpina 4366
 
0.3%
palustris 3874
 
0.3%
officinalis 3756
 
0.3%
orientalis 3679
 
0.3%
chinensis 3526
 
0.3%
japonica 3489
 
0.3%
Other values (47059) 1349323
96.8%
2025-02-13T13:04:38.490118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1669990
13.3%
i 1442888
11.5%
s 920248
 
7.3%
e 877799
 
7.0%
r 820749
 
6.5%
l 818240
 
6.5%
n 769710
 
6.1%
u 767100
 
6.1%
o 729082
 
5.8%
t 655726
 
5.2%
Other values (44) 3067698
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12539230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1669990
13.3%
i 1442888
11.5%
s 920248
 
7.3%
e 877799
 
7.0%
r 820749
 
6.5%
l 818240
 
6.5%
n 769710
 
6.1%
u 767100
 
6.1%
o 729082
 
5.8%
t 655726
 
5.2%
Other values (44) 3067698
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12539230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1669990
13.3%
i 1442888
11.5%
s 920248
 
7.3%
e 877799
 
7.0%
r 820749
 
6.5%
l 818240
 
6.5%
n 769710
 
6.1%
u 767100
 
6.1%
o 729082
 
5.8%
t 655726
 
5.2%
Other values (44) 3067698
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12539230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1669990
13.3%
i 1442888
11.5%
s 920248
 
7.3%
e 877799
 
7.0%
r 820749
 
6.5%
l 818240
 
6.5%
n 769710
 
6.1%
u 767100
 
6.1%
o 729082
 
5.8%
t 655726
 
5.2%
Other values (44) 3067698
24.5%

nomenclaturalCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2025-02-13T13:04:38.542156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5896616
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICBN
2nd rowICBN
3rd rowICBN
4th rowICBN
5th rowICBN
ValueCountFrequency (%)
icbn 1474154
100.0%
2025-02-13T13:04:38.635726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 1474154
25.0%
C 1474154
25.0%
B 1474154
25.0%
N 1474154
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5896616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1474154
25.0%
C 1474154
25.0%
B 1474154
25.0%
N 1474154
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5896616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1474154
25.0%
C 1474154
25.0%
B 1474154
25.0%
N 1474154
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5896616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1474154
25.0%
C 1474154
25.0%
B 1474154
25.0%
N 1474154
25.0%