Overview

Brought to you by YData

Dataset statistics

Number of variables75
Number of observations18866
Missing cells485211
Missing cells (%)34.3%
Total size in memory10.8 MiB
Average record size in memory600.0 B

Variable types

Text75

Dataset

DescriptionVertebrate Zoology Division - Mammalogy, Yale Peabody Museum 0061684-241126133413365
URLhttps://doi.org/10.15468/4mm6uc

Alerts

accessRights has constant value "Open Access, http://creativecommons.org/publicdomain/zero/1.0/; see Yale Peabody policies at: http://hdl.handle.net/10079/8931zqj" Constant
license has constant value "http://creativecommons.org/publicdomain/zero/1.0/" Constant
rightsHolder has constant value "Yale Peabody Museum" Constant
institutionCode has constant value "YPM" Constant
collectionCode has constant value "VZ" Constant
ownerInstitutionCode has constant value "YPM" Constant
basisOfRecord has constant value "PreservedSpecimen" Constant
dataGeneralizations has constant value "Coordinate data unavailable" Constant
kingdom has constant value "Animalia" Constant
phylum has constant value "Chordata" Constant
class has constant value "Mammalia" Constant
nomenclaturalCode has constant value "ICZN" Constant
taxonRemarks has constant value "Animals and Plants: Vertebrates - Mammals" Constant
dataGeneralizations has 18800 (99.7%) missing values Missing
recordedBy has 4296 (22.8%) missing values Missing
sex has 10118 (53.6%) missing values Missing
lifeStage has 17900 (94.9%) missing values Missing
reproductiveCondition has 16576 (87.9%) missing values Missing
behavior has 18864 (> 99.9%) missing values Missing
preparations has 349 (1.8%) missing values Missing
associatedMedia has 18411 (97.6%) missing values Missing
associatedReferences has 12450 (66.0%) missing values Missing
associatedTaxa has 18487 (98.0%) missing values Missing
otherCatalogNumbers has 12652 (67.1%) missing values Missing
fieldNumber has 11555 (61.2%) missing values Missing
eventDate has 6221 (33.0%) missing values Missing
year has 6267 (33.2%) missing values Missing
month has 7343 (38.9%) missing values Missing
day has 7899 (41.9%) missing values Missing
habitat has 18739 (99.3%) missing values Missing
higherGeography has 3778 (20.0%) missing values Missing
continent has 3913 (20.7%) missing values Missing
waterBody has 18739 (99.3%) missing values Missing
country has 3927 (20.8%) missing values Missing
stateProvince has 5347 (28.3%) missing values Missing
county has 9192 (48.7%) missing values Missing
municipality has 18309 (97.0%) missing values Missing
locality has 5869 (31.1%) missing values Missing
minimumElevationInMeters has 17391 (92.2%) missing values Missing
maximumElevationInMeters has 18082 (95.8%) missing values Missing
verbatimElevation has 17391 (92.2%) missing values Missing
decimalLatitude has 5543 (29.4%) missing values Missing
decimalLongitude has 5543 (29.4%) missing values Missing
geodeticDatum has 5666 (30.0%) missing values Missing
coordinateUncertaintyInMeters has 5609 (29.7%) missing values Missing
georeferencedBy has 18537 (98.3%) missing values Missing
georeferencedDate has 10549 (55.9%) missing values Missing
georeferenceProtocol has 5610 (29.7%) missing values Missing
georeferenceSources has 5615 (29.8%) missing values Missing
georeferenceRemarks has 5661 (30.0%) missing values Missing
typeStatus has 18844 (99.9%) missing values Missing
identifiedBy has 17735 (94.0%) missing values Missing
dateIdentified has 17913 (94.9%) missing values Missing
identificationRemarks has 18863 (> 99.9%) missing values Missing
order has 401 (2.1%) missing values Missing
family has 838 (4.4%) missing values Missing
genus has 1196 (6.3%) missing values Missing
specificEpithet has 2296 (12.2%) missing values Missing
infraspecificEpithet has 8470 (44.9%) missing values Missing
scientificNameAuthorship has 385 (2.0%) missing values Missing
gbifID has unique values Unique
bibliographicCitation has unique values Unique
references has unique values Unique
dynamicProperties has unique values Unique
occurrenceID has unique values Unique
catalogNumber has unique values Unique

Reproduction

Analysis started2025-02-28 17:47:41.865552
Analysis finished2025-02-28 17:47:43.186776
Duration1.32 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct18866
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:43.265383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters188660
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

Unique18866 ?
Unique (%)100.0%

Sample

1st row4953409301
2nd row4911830319
3rd row4911830318
4th row4911830317
5th row4911830316
ValueCountFrequency (%)
4953409301 1
 
< 0.1%
4599382340 1
 
< 0.1%
4911830315 1
 
< 0.1%
4911830314 1
 
< 0.1%
4911830313 1
 
< 0.1%
4911830312 1
 
< 0.1%
4911830311 1
 
< 0.1%
4911830310 1
 
< 0.1%
4911830309 1
 
< 0.1%
4911830308 1
 
< 0.1%
Other values (18856) 18856
99.9%
2025-02-28T12:47:43.437600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30292
16.1%
3 27042
14.3%
5 25137
13.3%
9 22536
11.9%
0 22490
11.9%
2 21472
11.4%
4 11335
 
6.0%
7 10804
 
5.7%
8 8933
 
4.7%
6 8619
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 30292
16.1%
3 27042
14.3%
5 25137
13.3%
9 22536
11.9%
0 22490
11.9%
2 21472
11.4%
4 11335
 
6.0%
7 10804
 
5.7%
8 8933
 
4.7%
6 8619
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 30292
16.1%
3 27042
14.3%
5 25137
13.3%
9 22536
11.9%
0 22490
11.9%
2 21472
11.4%
4 11335
 
6.0%
7 10804
 
5.7%
8 8933
 
4.7%
6 8619
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 30292
16.1%
3 27042
14.3%
5 25137
13.3%
9 22536
11.9%
0 22490
11.9%
2 21472
11.4%
4 11335
 
6.0%
7 10804
 
5.7%
8 8933
 
4.7%
6 8619
 
4.6%

accessRights
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:43.489787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length129
Median length129
Mean length129
Min length129

Characters and Unicode

Total characters2433714
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen Access, http://creativecommons.org/publicdomain/zero/1.0/; see Yale Peabody policies at: http://hdl.handle.net/10079/8931zqj
2nd rowOpen Access, http://creativecommons.org/publicdomain/zero/1.0/; see Yale Peabody policies at: http://hdl.handle.net/10079/8931zqj
3rd rowOpen Access, http://creativecommons.org/publicdomain/zero/1.0/; see Yale Peabody policies at: http://hdl.handle.net/10079/8931zqj
4th rowOpen Access, http://creativecommons.org/publicdomain/zero/1.0/; see Yale Peabody policies at: http://hdl.handle.net/10079/8931zqj
5th rowOpen Access, http://creativecommons.org/publicdomain/zero/1.0/; see Yale Peabody policies at: http://hdl.handle.net/10079/8931zqj
ValueCountFrequency (%)
open 18866
11.1%
access 18866
11.1%
http://creativecommons.org/publicdomain/zero/1.0 18866
11.1%
see 18866
11.1%
yale 18866
11.1%
peabody 18866
11.1%
policies 18866
11.1%
at 18866
11.1%
http://hdl.handle.net/10079/8931zqj 18866
11.1%
2025-02-28T12:47:43.574053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 226392
 
9.3%
/ 188660
 
7.8%
150928
 
6.2%
t 132062
 
5.4%
o 132062
 
5.4%
a 113196
 
4.7%
c 113196
 
4.7%
i 94330
 
3.9%
n 94330
 
3.9%
s 94330
 
3.9%
Other values (28) 1094228
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2433714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 226392
 
9.3%
/ 188660
 
7.8%
150928
 
6.2%
t 132062
 
5.4%
o 132062
 
5.4%
a 113196
 
4.7%
c 113196
 
4.7%
i 94330
 
3.9%
n 94330
 
3.9%
s 94330
 
3.9%
Other values (28) 1094228
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2433714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 226392
 
9.3%
/ 188660
 
7.8%
150928
 
6.2%
t 132062
 
5.4%
o 132062
 
5.4%
a 113196
 
4.7%
c 113196
 
4.7%
i 94330
 
3.9%
n 94330
 
3.9%
s 94330
 
3.9%
Other values (28) 1094228
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2433714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 226392
 
9.3%
/ 188660
 
7.8%
150928
 
6.2%
t 132062
 
5.4%
o 132062
 
5.4%
a 113196
 
4.7%
c 113196
 
4.7%
i 94330
 
3.9%
n 94330
 
3.9%
s 94330
 
3.9%
Other values (28) 1094228
45.0%
Distinct18866
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:43.706953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length62
Median length50
Mean length40.04675077
Min length20

Characters and Unicode

Total characters755522
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18866 ?
Unique (%)100.0%

Sample

1st rowTamias striatus fisheri (YPM MAM 017903)
2nd rowPeromyscus leucopus noveboracensis (YPM MAM 017889)
3rd rowPeromyscus leucopus noveboracensis (YPM MAM 017897)
4th rowPeromyscus leucopus noveboracensis (YPM MAM 017895)
5th rowPeromyscus leucopus noveboracensis (YPM MAM 017888)
ValueCountFrequency (%)
ypm 18866
 
18.4%
mam 18866
 
18.4%
peromyscus 1837
 
1.8%
cinereus 1489
 
1.5%
sorex 1193
 
1.2%
brevicauda 1125
 
1.1%
blarina 976
 
1.0%
zibethicus 898
 
0.9%
talpoides 868
 
0.8%
gapperi 848
 
0.8%
Other values (20938) 55590
54.2%
2025-02-28T12:47:43.920218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83690
 
11.1%
M 58523
 
7.7%
0 44332
 
5.9%
s 41623
 
5.5%
i 36625
 
4.8%
a 35093
 
4.6%
u 30890
 
4.1%
e 30381
 
4.0%
r 26522
 
3.5%
o 25267
 
3.3%
Other values (56) 342576
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 755522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83690
 
11.1%
M 58523
 
7.7%
0 44332
 
5.9%
s 41623
 
5.5%
i 36625
 
4.8%
a 35093
 
4.6%
u 30890
 
4.1%
e 30381
 
4.0%
r 26522
 
3.5%
o 25267
 
3.3%
Other values (56) 342576
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 755522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83690
 
11.1%
M 58523
 
7.7%
0 44332
 
5.9%
s 41623
 
5.5%
i 36625
 
4.8%
a 35093
 
4.6%
u 30890
 
4.1%
e 30381
 
4.0%
r 26522
 
3.5%
o 25267
 
3.3%
Other values (56) 342576
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 755522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83690
 
11.1%
M 58523
 
7.7%
0 44332
 
5.9%
s 41623
 
5.5%
i 36625
 
4.8%
a 35093
 
4.6%
u 30890
 
4.1%
e 30381
 
4.0%
r 26522
 
3.5%
o 25267
 
3.3%
Other values (56) 342576
45.3%

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:43.961614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length49
Median length49
Mean length49
Min length49

Characters and Unicode

Total characters924434
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://creativecommons.org/publicdomain/zero/1.0/
2nd rowhttp://creativecommons.org/publicdomain/zero/1.0/
3rd rowhttp://creativecommons.org/publicdomain/zero/1.0/
4th rowhttp://creativecommons.org/publicdomain/zero/1.0/
5th rowhttp://creativecommons.org/publicdomain/zero/1.0/
ValueCountFrequency (%)
http://creativecommons.org/publicdomain/zero/1.0 18866
100.0%
2025-02-28T12:47:44.043898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 113196
 
12.2%
o 94330
 
10.2%
m 56598
 
6.1%
c 56598
 
6.1%
r 56598
 
6.1%
e 56598
 
6.1%
t 56598
 
6.1%
i 56598
 
6.1%
. 37732
 
4.1%
n 37732
 
4.1%
Other values (14) 301856
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 924434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 113196
 
12.2%
o 94330
 
10.2%
m 56598
 
6.1%
c 56598
 
6.1%
r 56598
 
6.1%
e 56598
 
6.1%
t 56598
 
6.1%
i 56598
 
6.1%
. 37732
 
4.1%
n 37732
 
4.1%
Other values (14) 301856
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 924434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 113196
 
12.2%
o 94330
 
10.2%
m 56598
 
6.1%
c 56598
 
6.1%
r 56598
 
6.1%
e 56598
 
6.1%
t 56598
 
6.1%
i 56598
 
6.1%
. 37732
 
4.1%
n 37732
 
4.1%
Other values (14) 301856
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 924434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 113196
 
12.2%
o 94330
 
10.2%
m 56598
 
6.1%
c 56598
 
6.1%
r 56598
 
6.1%
e 56598
 
6.1%
t 56598
 
6.1%
i 56598
 
6.1%
. 37732
 
4.1%
n 37732
 
4.1%
Other values (14) 301856
32.7%
Distinct1200
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.074500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters452784
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

Unique667 ?
Unique (%)3.5%

Sample

1st row2024-10-14T12:59:55.000Z
2nd row2024-10-11T19:54:42.000Z
3rd row2024-10-11T19:54:42.000Z
4th row2024-10-11T19:54:42.000Z
5th row2024-10-11T19:54:42.000Z
ValueCountFrequency (%)
2024-09-17t21:33:28.000z 3971
21.0%
2024-10-12t17:36:53.000z 3555
18.8%
2024-09-29t10:06:24.000z 1799
 
9.5%
2024-09-23t19:57:36.000z 1572
 
8.3%
2024-02-19t13:33:41.000z 826
 
4.4%
2024-04-16t21:52:31.000z 553
 
2.9%
2024-04-28t21:51:52.000z 236
 
1.3%
2024-10-22t21:33:57.000z 219
 
1.2%
2023-07-18t22:00:07.000z 158
 
0.8%
2020-12-23t21:50:47.000z 157
 
0.8%
Other values (1190) 5820
30.8%
2025-02-28T12:47:44.159416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 104437
23.1%
2 68042
15.0%
1 42959
9.5%
- 37732
 
8.3%
: 37732
 
8.3%
3 29809
 
6.6%
4 22029
 
4.9%
T 18866
 
4.2%
. 18866
 
4.2%
Z 18866
 
4.2%
Other values (5) 53446
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 452784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 104437
23.1%
2 68042
15.0%
1 42959
9.5%
- 37732
 
8.3%
: 37732
 
8.3%
3 29809
 
6.6%
4 22029
 
4.9%
T 18866
 
4.2%
. 18866
 
4.2%
Z 18866
 
4.2%
Other values (5) 53446
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 452784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 104437
23.1%
2 68042
15.0%
1 42959
9.5%
- 37732
 
8.3%
: 37732
 
8.3%
3 29809
 
6.6%
4 22029
 
4.9%
T 18866
 
4.2%
. 18866
 
4.2%
Z 18866
 
4.2%
Other values (5) 53446
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 452784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 104437
23.1%
2 68042
15.0%
1 42959
9.5%
- 37732
 
8.3%
: 37732
 
8.3%
3 29809
 
6.6%
4 22029
 
4.9%
T 18866
 
4.2%
. 18866
 
4.2%
Z 18866
 
4.2%
Other values (5) 53446
11.8%

references
Text

Unique 

Distinct18866
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.211486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length64
Mean length64.95473338
Min length64

Characters and Unicode

Total characters1225436
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

Unique18866 ?
Unique (%)100.0%

Sample

1st rowhttp://collections.peabody.yale.edu/search/Record/YPM-MAM-017903
2nd rowhttp://collections.peabody.yale.edu/search/Record/YPM-MAM-017889
3rd rowhttp://collections.peabody.yale.edu/search/Record/YPM-MAM-017897
4th rowhttp://collections.peabody.yale.edu/search/Record/YPM-MAM-017895
5th rowhttp://collections.peabody.yale.edu/search/Record/YPM-MAM-017888
ValueCountFrequency (%)
http://collections.peabody.yale.edu/search/record/ypm-mam-017903 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017835 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017891 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017900 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017899 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017902 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017890 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017901 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017896 1
 
< 0.1%
http://collections.peabody.yale.edu/search/record/ypm-mam-017898 1
 
< 0.1%
Other values (18856) 18856
99.9%
2025-02-28T12:47:44.335976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 113196
 
9.2%
/ 94330
 
7.7%
c 75464
 
6.2%
o 75464
 
6.2%
. 61101
 
5.0%
M 56598
 
4.6%
t 56598
 
4.6%
l 56598
 
4.6%
d 56598
 
4.6%
a 56598
 
4.6%
Other values (25) 522891
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1225436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 113196
 
9.2%
/ 94330
 
7.7%
c 75464
 
6.2%
o 75464
 
6.2%
. 61101
 
5.0%
M 56598
 
4.6%
t 56598
 
4.6%
l 56598
 
4.6%
d 56598
 
4.6%
a 56598
 
4.6%
Other values (25) 522891
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1225436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 113196
 
9.2%
/ 94330
 
7.7%
c 75464
 
6.2%
o 75464
 
6.2%
. 61101
 
5.0%
M 56598
 
4.6%
t 56598
 
4.6%
l 56598
 
4.6%
d 56598
 
4.6%
a 56598
 
4.6%
Other values (25) 522891
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1225436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 113196
 
9.2%
/ 94330
 
7.7%
c 75464
 
6.2%
o 75464
 
6.2%
. 61101
 
5.0%
M 56598
 
4.6%
t 56598
 
4.6%
l 56598
 
4.6%
d 56598
 
4.6%
a 56598
 
4.6%
Other values (25) 522891
42.7%

rightsHolder
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.375507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters358454
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowYale Peabody Museum
2nd rowYale Peabody Museum
3rd rowYale Peabody Museum
4th rowYale Peabody Museum
5th rowYale Peabody Museum
ValueCountFrequency (%)
yale 18866
33.3%
peabody 18866
33.3%
museum 18866
33.3%
2025-02-28T12:47:44.458747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 56598
15.8%
a 37732
10.5%
37732
10.5%
u 37732
10.5%
Y 18866
 
5.3%
l 18866
 
5.3%
P 18866
 
5.3%
b 18866
 
5.3%
o 18866
 
5.3%
d 18866
 
5.3%
Other values (4) 75464
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 56598
15.8%
a 37732
10.5%
37732
10.5%
u 37732
10.5%
Y 18866
 
5.3%
l 18866
 
5.3%
P 18866
 
5.3%
b 18866
 
5.3%
o 18866
 
5.3%
d 18866
 
5.3%
Other values (4) 75464
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 56598
15.8%
a 37732
10.5%
37732
10.5%
u 37732
10.5%
Y 18866
 
5.3%
l 18866
 
5.3%
P 18866
 
5.3%
b 18866
 
5.3%
o 18866
 
5.3%
d 18866
 
5.3%
Other values (4) 75464
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 56598
15.8%
a 37732
10.5%
37732
10.5%
u 37732
10.5%
Y 18866
 
5.3%
l 18866
 
5.3%
P 18866
 
5.3%
b 18866
 
5.3%
o 18866
 
5.3%
d 18866
 
5.3%
Other values (4) 75464
21.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.488222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18866
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 row0
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
0 18321
97.1%
1 545
 
2.9%
2025-02-28T12:47:44.571531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18321
97.1%
1 545
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18321
97.1%
1 545
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18321
97.1%
1 545
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18321
97.1%
1 545
 
2.9%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.598779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters56598
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYPM
2nd rowYPM
3rd rowYPM
4th rowYPM
5th rowYPM
ValueCountFrequency (%)
ypm 18866
100.0%
2025-02-28T12:47:44.677625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.703940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters37732
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 rowVZ
2nd rowVZ
3rd rowVZ
4th rowVZ
5th rowVZ
ValueCountFrequency (%)
vz 18866
100.0%
2025-02-28T12:47:44.782538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V 18866
50.0%
Z 18866
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 18866
50.0%
Z 18866
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 18866
50.0%
Z 18866
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 18866
50.0%
Z 18866
50.0%

ownerInstitutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.810939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters56598
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYPM
2nd rowYPM
3rd rowYPM
4th rowYPM
5th rowYPM
ValueCountFrequency (%)
ypm 18866
100.0%
2025-02-28T12:47:44.889881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 18866
33.3%
P 18866
33.3%
M 18866
33.3%

basisOfRecord
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:44.976714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters320722
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 18866
100.0%
2025-02-28T12:47:45.056276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 94330
29.4%
r 37732
 
11.8%
P 18866
 
5.9%
s 18866
 
5.9%
v 18866
 
5.9%
d 18866
 
5.9%
S 18866
 
5.9%
p 18866
 
5.9%
c 18866
 
5.9%
i 18866
 
5.9%
Other values (2) 37732
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 94330
29.4%
r 37732
 
11.8%
P 18866
 
5.9%
s 18866
 
5.9%
v 18866
 
5.9%
d 18866
 
5.9%
S 18866
 
5.9%
p 18866
 
5.9%
c 18866
 
5.9%
i 18866
 
5.9%
Other values (2) 37732
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 94330
29.4%
r 37732
 
11.8%
P 18866
 
5.9%
s 18866
 
5.9%
v 18866
 
5.9%
d 18866
 
5.9%
S 18866
 
5.9%
p 18866
 
5.9%
c 18866
 
5.9%
i 18866
 
5.9%
Other values (2) 37732
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 94330
29.4%
r 37732
 
11.8%
P 18866
 
5.9%
s 18866
 
5.9%
v 18866
 
5.9%
d 18866
 
5.9%
S 18866
 
5.9%
p 18866
 
5.9%
c 18866
 
5.9%
i 18866
 
5.9%
Other values (2) 37732
 
11.8%

dataGeneralizations
Text

Constant  Missing 

Distinct1
Distinct (%)1.5%
Missing18800
Missing (%)99.7%
Memory size147.5 KiB
2025-02-28T12:47:45.084459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters1782
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoordinate data unavailable
2nd rowCoordinate data unavailable
3rd rowCoordinate data unavailable
4th rowCoordinate data unavailable
5th rowCoordinate data unavailable
ValueCountFrequency (%)
coordinate 66
33.3%
data 66
33.3%
unavailable 66
33.3%
2025-02-28T12:47:45.163856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 396
22.2%
o 132
 
7.4%
d 132
 
7.4%
i 132
 
7.4%
n 132
 
7.4%
t 132
 
7.4%
e 132
 
7.4%
132
 
7.4%
l 132
 
7.4%
C 66
 
3.7%
Other values (4) 264
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 396
22.2%
o 132
 
7.4%
d 132
 
7.4%
i 132
 
7.4%
n 132
 
7.4%
t 132
 
7.4%
e 132
 
7.4%
132
 
7.4%
l 132
 
7.4%
C 66
 
3.7%
Other values (4) 264
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 396
22.2%
o 132
 
7.4%
d 132
 
7.4%
i 132
 
7.4%
n 132
 
7.4%
t 132
 
7.4%
e 132
 
7.4%
132
 
7.4%
l 132
 
7.4%
C 66
 
3.7%
Other values (4) 264
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 396
22.2%
o 132
 
7.4%
d 132
 
7.4%
i 132
 
7.4%
n 132
 
7.4%
t 132
 
7.4%
e 132
 
7.4%
132
 
7.4%
l 132
 
7.4%
C 66
 
3.7%
Other values (4) 264
14.8%

dynamicProperties
Text

Unique 

Distinct18866
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:45.202143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1073
Median length877
Mean length64.79444503
Min length19

Characters and Unicode

Total characters1222412
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18866 ?
Unique (%)100.0%

Sample

1st row{ "irn": "2495311" }
2nd row{ "irn": "2489043", "media": "1223142:2398869c-63eb-410d-8cf8-205d5aacbfcd", "mm_repository_id": "1223142" }
3rd row{ "irn": "2489051", "media": "1223150:ed40315a-fb57-4421-a251-a7ede5b38478", "mm_repository_id": "1223150" }
4th row{ "irn": "2489049", "media": "1223148:3d1eee9f-f1e6-4948-b842-640fbf489e2a", "mm_repository_id": "1223148" }
5th row{ "irn": "2489042", "media": "1223141:56aefa44-5e83-4aec-83f3-b632bc2756cf", "mm_repository_id": "1223141" }
ValueCountFrequency (%)
38111
29.9%
irn 18866
14.8%
solr_long_lat 13323
 
10.5%
original_num 6214
 
4.9%
osteo 4381
 
3.4%
mm_repository_id 455
 
0.4%
media 455
 
0.4%
related_record_links 379
 
0.3%
related_record_types 379
 
0.3%
71.273830,44.049466 311
 
0.2%
Other values (33627) 44501
34.9%
2025-02-28T12:47:45.306943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 160284
 
13.1%
108509
 
8.9%
1 48769
 
4.0%
l 47322
 
3.9%
n 44996
 
3.7%
0 44312
 
3.6%
4 43624
 
3.6%
r 41589
 
3.4%
: 41225
 
3.4%
3 41094
 
3.4%
Other values (56) 600688
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1222412
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 160284
 
13.1%
108509
 
8.9%
1 48769
 
4.0%
l 47322
 
3.9%
n 44996
 
3.7%
0 44312
 
3.6%
4 43624
 
3.6%
r 41589
 
3.4%
: 41225
 
3.4%
3 41094
 
3.4%
Other values (56) 600688
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1222412
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 160284
 
13.1%
108509
 
8.9%
1 48769
 
4.0%
l 47322
 
3.9%
n 44996
 
3.7%
0 44312
 
3.6%
4 43624
 
3.6%
r 41589
 
3.4%
: 41225
 
3.4%
3 41094
 
3.4%
Other values (56) 600688
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1222412
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 160284
 
13.1%
108509
 
8.9%
1 48769
 
4.0%
l 47322
 
3.9%
n 44996
 
3.7%
0 44312
 
3.6%
4 43624
 
3.6%
r 41589
 
3.4%
: 41225
 
3.4%
3 41094
 
3.4%
Other values (56) 600688
49.1%

occurrenceID
Text

Unique 

Distinct18866
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:45.361701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters848970
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

Unique18866 ?
Unique (%)100.0%

Sample

1st rowurn:uuid:ef710e32-eb63-4875-b9d8-f21a261c1f52
2nd rowurn:uuid:2df9a10d-0595-4c2d-bb13-43b6677a15ce
3rd rowurn:uuid:35474ea7-f956-4872-88c2-a8c56cbe9f90
4th rowurn:uuid:6eaa6b8b-f8a1-44ee-b671-1a734de9ada2
5th rowurn:uuid:b45e450f-3835-46af-be66-6494f44d014e
ValueCountFrequency (%)
urn:uuid:ef710e32-eb63-4875-b9d8-f21a261c1f52 1
 
< 0.1%
urn:uuid:7a7bd1dd-0c61-423e-8d79-316ae9466af3 1
 
< 0.1%
urn:uuid:c2221631-94d5-4364-b7a1-6e8875d768ba 1
 
< 0.1%
urn:uuid:565e73ca-2d43-4f72-bf13-66ca168617ad 1
 
< 0.1%
urn:uuid:8ebc41fa-c154-4c27-a7d4-606e62b2dc95 1
 
< 0.1%
urn:uuid:9ba9abd0-a03f-49c3-97e5-d8a6557c42bd 1
 
< 0.1%
urn:uuid:183dfe30-8155-4c5d-ae5d-15cc0b7ea3b8 1
 
< 0.1%
urn:uuid:fa9cc82d-fccf-4fb9-834c-a5e890e5ff61 1
 
< 0.1%
urn:uuid:b4b795a6-619a-4d62-b9a2-3c911f103ed3 1
 
< 0.1%
urn:uuid:a57c6e6c-6f11-465a-a440-a5953d2cf9d2 1
 
< 0.1%
Other values (18856) 18856
99.9%
2025-02-28T12:47:45.482527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 75464
 
8.9%
u 56598
 
6.7%
4 54374
 
6.4%
d 54159
 
6.4%
8 40303
 
4.7%
9 40140
 
4.7%
b 40080
 
4.7%
a 39808
 
4.7%
: 37732
 
4.4%
f 35654
 
4.2%
Other values (12) 374658
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 848970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 75464
 
8.9%
u 56598
 
6.7%
4 54374
 
6.4%
d 54159
 
6.4%
8 40303
 
4.7%
9 40140
 
4.7%
b 40080
 
4.7%
a 39808
 
4.7%
: 37732
 
4.4%
f 35654
 
4.2%
Other values (12) 374658
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 848970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 75464
 
8.9%
u 56598
 
6.7%
4 54374
 
6.4%
d 54159
 
6.4%
8 40303
 
4.7%
9 40140
 
4.7%
b 40080
 
4.7%
a 39808
 
4.7%
: 37732
 
4.4%
f 35654
 
4.2%
Other values (12) 374658
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 848970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 75464
 
8.9%
u 56598
 
6.7%
4 54374
 
6.4%
d 54159
 
6.4%
8 40303
 
4.7%
9 40140
 
4.7%
b 40080
 
4.7%
a 39808
 
4.7%
: 37732
 
4.4%
f 35654
 
4.2%
Other values (12) 374658
44.1%

catalogNumber
Text

Unique 

Distinct18866
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:45.627716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.95473338
Min length14

Characters and Unicode

Total characters282136
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

Unique18866 ?
Unique (%)100.0%

Sample

1st rowYPM MAM 017903
2nd rowYPM MAM 017889
3rd rowYPM MAM 017897
4th rowYPM MAM 017895
5th rowYPM MAM 017888
ValueCountFrequency (%)
ypm 18866
33.3%
mam 18866
33.3%
015555.002 1
 
< 0.1%
017813 1
 
< 0.1%
017899 1
 
< 0.1%
017902 1
 
< 0.1%
017890 1
 
< 0.1%
017901 1
 
< 0.1%
017896 1
 
< 0.1%
017898 1
 
< 0.1%
Other values (18858) 18858
33.3%
2025-02-28T12:47:45.845974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 56598
20.1%
0 44332
15.7%
37732
13.4%
1 20061
 
7.1%
Y 18866
 
6.7%
P 18866
 
6.7%
A 18866
 
6.7%
2 9843
 
3.5%
6 8199
 
2.9%
7 8066
 
2.9%
Other values (6) 40707
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 282136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 56598
20.1%
0 44332
15.7%
37732
13.4%
1 20061
 
7.1%
Y 18866
 
6.7%
P 18866
 
6.7%
A 18866
 
6.7%
2 9843
 
3.5%
6 8199
 
2.9%
7 8066
 
2.9%
Other values (6) 40707
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 282136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 56598
20.1%
0 44332
15.7%
37732
13.4%
1 20061
 
7.1%
Y 18866
 
6.7%
P 18866
 
6.7%
A 18866
 
6.7%
2 9843
 
3.5%
6 8199
 
2.9%
7 8066
 
2.9%
Other values (6) 40707
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 282136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 56598
20.1%
0 44332
15.7%
37732
13.4%
1 20061
 
7.1%
Y 18866
 
6.7%
P 18866
 
6.7%
A 18866
 
6.7%
2 9843
 
3.5%
6 8199
 
2.9%
7 8066
 
2.9%
Other values (6) 40707
14.4%

recordedBy
Text

Missing 

Distinct1050
Distinct (%)7.2%
Missing4296
Missing (%)22.8%
Memory size147.5 KiB
2025-02-28T12:47:45.988890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length120
Median length80
Mean length16.20549073
Min length3

Characters and Unicode

Total characters236114
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)3.6%

Sample

1st rowRichard E. Boardman, Kristof Zyskowski
2nd rowRichard E. Boardman
3rd rowLourdes M. Rojas
4th rowRichard E. Boardman
5th rowRichard E. Boardman
ValueCountFrequency (%)
mariko 1875
 
4.7%
yamasaki 1875
 
4.7%
e 1394
 
3.5%
b 1115
 
2.8%
c 1091
 
2.7%
j 1070
 
2.7%
a 867
 
2.2%
ryan 849
 
2.1%
stephens 848
 
2.1%
d 830
 
2.1%
Other values (1289) 28092
70.4%
2025-02-28T12:47:46.207199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25336
 
10.7%
a 21256
 
9.0%
e 16815
 
7.1%
r 13987
 
5.9%
i 13506
 
5.7%
o 12209
 
5.2%
n 11603
 
4.9%
. 10574
 
4.5%
l 9856
 
4.2%
s 8143
 
3.4%
Other values (59) 92829
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 236114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25336
 
10.7%
a 21256
 
9.0%
e 16815
 
7.1%
r 13987
 
5.9%
i 13506
 
5.7%
o 12209
 
5.2%
n 11603
 
4.9%
. 10574
 
4.5%
l 9856
 
4.2%
s 8143
 
3.4%
Other values (59) 92829
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 236114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25336
 
10.7%
a 21256
 
9.0%
e 16815
 
7.1%
r 13987
 
5.9%
i 13506
 
5.7%
o 12209
 
5.2%
n 11603
 
4.9%
. 10574
 
4.5%
l 9856
 
4.2%
s 8143
 
3.4%
Other values (59) 92829
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 236114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25336
 
10.7%
a 21256
 
9.0%
e 16815
 
7.1%
r 13987
 
5.9%
i 13506
 
5.7%
o 12209
 
5.2%
n 11603
 
4.9%
. 10574
 
4.5%
l 9856
 
4.2%
s 8143
 
3.4%
Other values (59) 92829
39.3%
Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:46.249036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.000318032
Min length1

Characters and Unicode

Total characters18872
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

Unique8 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1 18844
99.9%
2 5
 
< 0.1%
3 4
 
< 0.1%
6 3
 
< 0.1%
11 2
 
< 0.1%
17 1
 
< 0.1%
10 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
2025-02-28T12:47:46.331852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18850
99.9%
2 6
 
< 0.1%
3 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
0 1
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18850
99.9%
2 6
 
< 0.1%
3 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
0 1
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18850
99.9%
2 6
 
< 0.1%
3 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
0 1
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18850
99.9%
2 6
 
< 0.1%
3 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
0 1
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%

sex
Text

Missing 

Distinct11
Distinct (%)0.1%
Missing10118
Missing (%)53.6%
Memory size147.5 KiB
2025-02-28T12:47:46.360427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length4
Mean length5.222336534
Min length4

Characters and Unicode

Total characters45685
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale
ValueCountFrequency (%)
male 5012
54.4%
female 4182
45.4%
unknown 10
 
0.1%
undeterminable 1
 
< 0.1%
2025-02-28T12:47:46.441535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13379
29.3%
m 9195
20.1%
l 9195
20.1%
a 9195
20.1%
f 4182
 
9.2%
457
 
1.0%
n 32
 
0.1%
u 11
 
< 0.1%
w 10
 
< 0.1%
k 10
 
< 0.1%
Other values (7) 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13379
29.3%
m 9195
20.1%
l 9195
20.1%
a 9195
20.1%
f 4182
 
9.2%
457
 
1.0%
n 32
 
0.1%
u 11
 
< 0.1%
w 10
 
< 0.1%
k 10
 
< 0.1%
Other values (7) 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13379
29.3%
m 9195
20.1%
l 9195
20.1%
a 9195
20.1%
f 4182
 
9.2%
457
 
1.0%
n 32
 
0.1%
u 11
 
< 0.1%
w 10
 
< 0.1%
k 10
 
< 0.1%
Other values (7) 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13379
29.3%
m 9195
20.1%
l 9195
20.1%
a 9195
20.1%
f 4182
 
9.2%
457
 
1.0%
n 32
 
0.1%
u 11
 
< 0.1%
w 10
 
< 0.1%
k 10
 
< 0.1%
Other values (7) 19
 
< 0.1%

lifeStage
Text

Missing 

Distinct16
Distinct (%)1.7%
Missing17900
Missing (%)94.9%
Memory size147.5 KiB
2025-02-28T12:47:46.472013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length5
Mean length6.195652174
Min length5

Characters and Unicode

Total characters5985
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

Unique5 ?
Unique (%)0.5%

Sample

1st rowadult
2nd rowadult
3rd rowadult
4th rowadult
5th rowadult
ValueCountFrequency (%)
adult 583
57.0%
juvenile 203
 
19.9%
young 149
 
14.6%
immature 29
 
2.8%
neonate 26
 
2.5%
subadult 23
 
2.3%
fetal 7
 
0.7%
embryo 2
 
0.2%
2025-02-28T12:47:46.556981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 1010
16.9%
l 816
13.6%
a 668
11.2%
t 668
11.2%
d 606
10.1%
e 496
8.3%
n 404
 
6.8%
i 232
 
3.9%
v 203
 
3.4%
j 203
 
3.4%
Other values (9) 679
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1010
16.9%
l 816
13.6%
a 668
11.2%
t 668
11.2%
d 606
10.1%
e 496
8.3%
n 404
 
6.8%
i 232
 
3.9%
v 203
 
3.4%
j 203
 
3.4%
Other values (9) 679
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1010
16.9%
l 816
13.6%
a 668
11.2%
t 668
11.2%
d 606
10.1%
e 496
8.3%
n 404
 
6.8%
i 232
 
3.9%
v 203
 
3.4%
j 203
 
3.4%
Other values (9) 679
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1010
16.9%
l 816
13.6%
a 668
11.2%
t 668
11.2%
d 606
10.1%
e 496
8.3%
n 404
 
6.8%
i 232
 
3.9%
v 203
 
3.4%
j 203
 
3.4%
Other values (9) 679
11.3%

reproductiveCondition
Text

Missing 

Distinct626
Distinct (%)27.3%
Missing16576
Missing (%)87.9%
Memory size147.5 KiB
2025-02-28T12:47:46.663991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length166
Median length116
Mean length12.40349345
Min length2

Characters and Unicode

Total characters28404
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique457 ?
Unique (%)20.0%

Sample

1st rowtestes 5 x 2 mm
2nd rowEMB; 6; 10x8
3rd rowSCR; L=6x4
4th rowSCR R=8x5
5th rowEMB; L=4; R=2, 14X18
ValueCountFrequency (%)
testes 1006
16.2%
mm 877
 
14.1%
embryo 650
 
10.4%
no 643
 
10.3%
3 151
 
2.4%
2 137
 
2.2%
embryos 137
 
2.2%
lactating 137
 
2.2%
4 135
 
2.2%
5 111
 
1.8%
Other values (469) 2242
36.0%
2025-02-28T12:47:46.846212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3936
13.9%
e 3252
11.4%
m 2969
 
10.5%
s 2466
 
8.7%
t 2401
 
8.5%
o 1669
 
5.9%
r 1048
 
3.7%
n 966
 
3.4%
b 824
 
2.9%
y 821
 
2.9%
Other values (60) 8052
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3936
13.9%
e 3252
11.4%
m 2969
 
10.5%
s 2466
 
8.7%
t 2401
 
8.5%
o 1669
 
5.9%
r 1048
 
3.7%
n 966
 
3.4%
b 824
 
2.9%
y 821
 
2.9%
Other values (60) 8052
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3936
13.9%
e 3252
11.4%
m 2969
 
10.5%
s 2466
 
8.7%
t 2401
 
8.5%
o 1669
 
5.9%
r 1048
 
3.7%
n 966
 
3.4%
b 824
 
2.9%
y 821
 
2.9%
Other values (60) 8052
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3936
13.9%
e 3252
11.4%
m 2969
 
10.5%
s 2466
 
8.7%
t 2401
 
8.5%
o 1669
 
5.9%
r 1048
 
3.7%
n 966
 
3.4%
b 824
 
2.9%
y 821
 
2.9%
Other values (60) 8052
28.3%

behavior
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing18864
Missing (%)> 99.9%
Memory size147.5 KiB
2025-02-28T12:47:46.887664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length64
Median length56.5
Mean length56.5
Min length49

Characters and Unicode

Total characters113
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

Unique2 ?
Unique (%)100.0%

Sample

1st rowwas calling while hanging from a 0.5 m tall shrub
2nd rowwas day-roosting in a dense subcanopy tree ca. 15 m above ground
ValueCountFrequency (%)
was 2
 
9.1%
a 2
 
9.1%
m 2
 
9.1%
in 1
 
4.5%
above 1
 
4.5%
15 1
 
4.5%
ca 1
 
4.5%
tree 1
 
4.5%
subcanopy 1
 
4.5%
dense 1
 
4.5%
Other values (9) 9
40.9%
2025-02-28T12:47:46.984912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
17.7%
a 11
 
9.7%
n 8
 
7.1%
o 6
 
5.3%
s 6
 
5.3%
e 6
 
5.3%
l 5
 
4.4%
i 5
 
4.4%
g 5
 
4.4%
r 5
 
4.4%
Other values (17) 36
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20
17.7%
a 11
 
9.7%
n 8
 
7.1%
o 6
 
5.3%
s 6
 
5.3%
e 6
 
5.3%
l 5
 
4.4%
i 5
 
4.4%
g 5
 
4.4%
r 5
 
4.4%
Other values (17) 36
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20
17.7%
a 11
 
9.7%
n 8
 
7.1%
o 6
 
5.3%
s 6
 
5.3%
e 6
 
5.3%
l 5
 
4.4%
i 5
 
4.4%
g 5
 
4.4%
r 5
 
4.4%
Other values (17) 36
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20
17.7%
a 11
 
9.7%
n 8
 
7.1%
o 6
 
5.3%
s 6
 
5.3%
e 6
 
5.3%
l 5
 
4.4%
i 5
 
4.4%
g 5
 
4.4%
r 5
 
4.4%
Other values (17) 36
31.9%

preparations
Text

Missing 

Distinct1019
Distinct (%)5.5%
Missing349
Missing (%)1.8%
Memory size147.5 KiB
2025-02-28T12:47:47.020938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length262
Median length190
Mean length25.19781822
Min length4

Characters and Unicode

Total characters466588
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique762 ?
Unique (%)4.1%

Sample

1st rowskin, round; skull; tissue (frozen)
2nd rowtissue (frozen)
3rd rowtissue (frozen)
4th rowtissue (frozen)
5th rowtissue (frozen)
ValueCountFrequency (%)
skeleton 13111
20.4%
skull 8315
12.9%
only 7454
11.6%
skin 6927
10.8%
round 5887
9.2%
tissue 4575
 
7.1%
frozen 4435
 
6.9%
incomplete 1443
 
2.2%
alc 1212
 
1.9%
10 1172
 
1.8%
Other values (1014) 9705
15.1%
2025-02-28T12:47:47.127445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45719
 
9.8%
n 43643
 
9.4%
e 42409
 
9.1%
l 42371
 
9.1%
s 40001
 
8.6%
o 35814
 
7.7%
k 28618
 
6.1%
t 22389
 
4.8%
u 19801
 
4.2%
i 16215
 
3.5%
Other values (70) 129608
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 466588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45719
 
9.8%
n 43643
 
9.4%
e 42409
 
9.1%
l 42371
 
9.1%
s 40001
 
8.6%
o 35814
 
7.7%
k 28618
 
6.1%
t 22389
 
4.8%
u 19801
 
4.2%
i 16215
 
3.5%
Other values (70) 129608
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 466588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45719
 
9.8%
n 43643
 
9.4%
e 42409
 
9.1%
l 42371
 
9.1%
s 40001
 
8.6%
o 35814
 
7.7%
k 28618
 
6.1%
t 22389
 
4.8%
u 19801
 
4.2%
i 16215
 
3.5%
Other values (70) 129608
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 466588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45719
 
9.8%
n 43643
 
9.4%
e 42409
 
9.1%
l 42371
 
9.1%
s 40001
 
8.6%
o 35814
 
7.7%
k 28618
 
6.1%
t 22389
 
4.8%
u 19801
 
4.2%
i 16215
 
3.5%
Other values (70) 129608
27.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:47.157079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.98484045
Min length7

Characters and Unicode

Total characters244972
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowin collection
2nd rowin collection
3rd rowin collection
4th rowin collection
5th rowin collection
ValueCountFrequency (%)
in 18804
49.8%
collection 18804
49.8%
on 62
 
0.2%
loan 38
 
0.1%
not 14
 
< 0.1%
view 14
 
< 0.1%
exhibit 10
 
< 0.1%
2025-02-28T12:47:47.243861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 37722
15.4%
o 37722
15.4%
l 37646
15.4%
i 37642
15.4%
c 37608
15.4%
18880
7.7%
e 18828
7.7%
t 18828
7.7%
a 38
 
< 0.1%
v 14
 
< 0.1%
Other values (4) 44
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 37722
15.4%
o 37722
15.4%
l 37646
15.4%
i 37642
15.4%
c 37608
15.4%
18880
7.7%
e 18828
7.7%
t 18828
7.7%
a 38
 
< 0.1%
v 14
 
< 0.1%
Other values (4) 44
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 37722
15.4%
o 37722
15.4%
l 37646
15.4%
i 37642
15.4%
c 37608
15.4%
18880
7.7%
e 18828
7.7%
t 18828
7.7%
a 38
 
< 0.1%
v 14
 
< 0.1%
Other values (4) 44
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 37722
15.4%
o 37722
15.4%
l 37646
15.4%
i 37642
15.4%
c 37608
15.4%
18880
7.7%
e 18828
7.7%
t 18828
7.7%
a 38
 
< 0.1%
v 14
 
< 0.1%
Other values (4) 44
 
< 0.1%

associatedMedia
Text

Missing 

Distinct455
Distinct (%)100.0%
Missing18411
Missing (%)97.6%
Memory size147.5 KiB
2025-02-28T12:47:47.299461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length113
Median length113
Mean length110.4417582
Min length107

Characters and Unicode

Total characters50251
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

Unique455 ?
Unique (%)100.0%

Sample

1st rowhttps://images.collections.yale.edu/iiif/2/ypm:2398869c-63eb-410d-8cf8-205d5aacbfcd/full/!1920,1920/0/default.jpg
2nd rowhttps://images.collections.yale.edu/iiif/2/ypm:ed40315a-fb57-4421-a251-a7ede5b38478/full/!1920,1920/0/default.jpg
3rd rowhttps://images.collections.yale.edu/iiif/2/ypm:3d1eee9f-f1e6-4948-b842-640fbf489e2a/full/!1920,1920/0/default.jpg
4th rowhttps://images.collections.yale.edu/iiif/2/ypm:56aefa44-5e83-4aec-83f3-b632bc2756cf/full/!1920,1920/0/default.jpg
5th rowhttps://images.collections.yale.edu/iiif/2/ypm:ebedb256-ea73-46ab-ae98-27dbcdccc9d5/full/!1920,1920/0/default.jpg
ValueCountFrequency (%)
https://images.collections.yale.edu/iiif/2/ypm:4ef97955-2b97-4cd3-a7fb-0a03a196b4dd/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:de7f1b1a-a7d4-4670-bc54-9849bb3d9d8c/full/full/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:ed40315a-fb57-4421-a251-a7ede5b38478/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:3d1eee9f-f1e6-4948-b842-640fbf489e2a/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:56aefa44-5e83-4aec-83f3-b632bc2756cf/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:ebedb256-ea73-46ab-ae98-27dbcdccc9d5/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:67a707fa-ae74-4349-ad09-2e55a1a5589e/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:7d320417-0c05-49e7-9e7e-72deae2280ad/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:799d4b82-3a70-4f4e-af19-38e53ab2c90f/full/!1920,1920/0/default.jpg 1
 
0.2%
https://images.collections.yale.edu/iiif/2/ypm:7338d268-6b2a-4d3f-8caf-73ad8e7818f6/full/!1920,1920/0/default.jpg 1
 
0.2%
Other values (445) 445
97.8%
2025-02-28T12:47:47.496938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4095
 
8.1%
l 3118
 
6.2%
e 3113
 
6.2%
f 2407
 
4.8%
a 2326
 
4.6%
i 2275
 
4.5%
2 1846
 
3.7%
. 1820
 
3.6%
- 1820
 
3.6%
t 1820
 
3.6%
Other values (25) 25611
51.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 4095
 
8.1%
l 3118
 
6.2%
e 3113
 
6.2%
f 2407
 
4.8%
a 2326
 
4.6%
i 2275
 
4.5%
2 1846
 
3.7%
. 1820
 
3.6%
- 1820
 
3.6%
t 1820
 
3.6%
Other values (25) 25611
51.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 4095
 
8.1%
l 3118
 
6.2%
e 3113
 
6.2%
f 2407
 
4.8%
a 2326
 
4.6%
i 2275
 
4.5%
2 1846
 
3.7%
. 1820
 
3.6%
- 1820
 
3.6%
t 1820
 
3.6%
Other values (25) 25611
51.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 4095
 
8.1%
l 3118
 
6.2%
e 3113
 
6.2%
f 2407
 
4.8%
a 2326
 
4.6%
i 2275
 
4.5%
2 1846
 
3.7%
. 1820
 
3.6%
- 1820
 
3.6%
t 1820
 
3.6%
Other values (25) 25611
51.0%

associatedReferences
Text

Missing 

Distinct178
Distinct (%)2.8%
Missing12450
Missing (%)66.0%
Memory size147.5 KiB
2025-02-28T12:47:47.544194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length116
Median length1
Mean length8.085099751
Min length1

Characters and Unicode

Total characters51874
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)1.2%

Sample

1st row|
2nd row|
3rd row|
4th row|
5th row|
ValueCountFrequency (%)
4933
37.6%
by 1565
 
11.9%
det 1461
 
11.1%
kristof 303
 
2.3%
jordan 300
 
2.3%
g 300
 
2.3%
colosi 300
 
2.3%
a 296
 
2.3%
zyskowski 291
 
2.2%
mary 288
 
2.2%
Other values (171) 3078
23.5%
2025-02-28T12:47:47.651054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
| 7591
 
14.6%
6699
 
12.9%
e 2799
 
5.4%
. 2603
 
5.0%
r 2373
 
4.6%
y 2262
 
4.4%
t 2241
 
4.3%
o 2076
 
4.0%
b 1743
 
3.4%
D 1738
 
3.4%
Other values (55) 19749
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
| 7591
 
14.6%
6699
 
12.9%
e 2799
 
5.4%
. 2603
 
5.0%
r 2373
 
4.6%
y 2262
 
4.4%
t 2241
 
4.3%
o 2076
 
4.0%
b 1743
 
3.4%
D 1738
 
3.4%
Other values (55) 19749
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
| 7591
 
14.6%
6699
 
12.9%
e 2799
 
5.4%
. 2603
 
5.0%
r 2373
 
4.6%
y 2262
 
4.4%
t 2241
 
4.3%
o 2076
 
4.0%
b 1743
 
3.4%
D 1738
 
3.4%
Other values (55) 19749
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
| 7591
 
14.6%
6699
 
12.9%
e 2799
 
5.4%
. 2603
 
5.0%
r 2373
 
4.6%
y 2262
 
4.4%
t 2241
 
4.3%
o 2076
 
4.0%
b 1743
 
3.4%
D 1738
 
3.4%
Other values (55) 19749
38.1%

associatedTaxa
Text

Missing 

Distinct373
Distinct (%)98.4%
Missing18487
Missing (%)98.0%
Memory size147.5 KiB
2025-02-28T12:47:47.686045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length131
Median length10
Mean length13.77572559
Min length10

Characters and Unicode

Total characters5221
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique371 ?
Unique (%)97.9%

Sample

1st rowENT.013766
2nd rowoffspring: MAM.015755
3rd rowparent: MAM.015754
4th rowMAM.001438
5th rowMAM.004953
ValueCountFrequency (%)
part 39
 
6.9%
same 36
 
6.3%
specimen 36
 
6.3%
of 36
 
6.3%
other 8
 
1.4%
parent 7
 
1.2%
mam.012670 6
 
1.1%
skeleton 3
 
0.5%
mam.013246|part 3
 
0.5%
mam.013247|part 3
 
0.5%
Other values (381) 392
68.9%
2025-02-28T12:47:47.779520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 884
16.9%
M 775
14.8%
. 402
 
7.7%
A 391
 
7.5%
1 344
 
6.6%
3 195
 
3.7%
190
 
3.6%
9 173
 
3.3%
2 166
 
3.2%
5 148
 
2.8%
Other values (34) 1553
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 884
16.9%
M 775
14.8%
. 402
 
7.7%
A 391
 
7.5%
1 344
 
6.6%
3 195
 
3.7%
190
 
3.6%
9 173
 
3.3%
2 166
 
3.2%
5 148
 
2.8%
Other values (34) 1553
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 884
16.9%
M 775
14.8%
. 402
 
7.7%
A 391
 
7.5%
1 344
 
6.6%
3 195
 
3.7%
190
 
3.6%
9 173
 
3.3%
2 166
 
3.2%
5 148
 
2.8%
Other values (34) 1553
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 884
16.9%
M 775
14.8%
. 402
 
7.7%
A 391
 
7.5%
1 344
 
6.6%
3 195
 
3.7%
190
 
3.6%
9 173
 
3.3%
2 166
 
3.2%
5 148
 
2.8%
Other values (34) 1553
29.7%

otherCatalogNumbers
Text

Missing 

Distinct6197
Distinct (%)99.7%
Missing12652
Missing (%)67.1%
Memory size147.5 KiB
2025-02-28T12:47:47.819068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length232
Median length128
Mean length20.18828452
Min length3

Characters and Unicode

Total characters125450
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

Unique6180 ?
Unique (%)99.5%

Sample

1st rowOsteo 12753 (MAM.O.12753)
2nd rowOsteo 2583 (MAM.O.02583)
3rd rowOsteo 3875 (MAM.O.03875)
4th rowVP.061504
5th rowUAM 112553
ValueCountFrequency (%)
osteo 4594
28.9%
11593 14
 
0.1%
m 6
 
< 0.1%
dcm 5
 
< 0.1%
uam 5
 
< 0.1%
s 5
 
< 0.1%
3978 4
 
< 0.1%
295 3
 
< 0.1%
10180 3
 
< 0.1%
14460 3
 
< 0.1%
Other values (10931) 11236
70.8%
2025-02-28T12:47:47.919021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 10298
 
8.2%
M 10211
 
8.1%
9664
 
7.7%
O 9191
 
7.3%
1 8952
 
7.1%
0 8355
 
6.7%
3 6022
 
4.8%
4 5451
 
4.3%
2 5128
 
4.1%
A 5092
 
4.1%
Other values (45) 47086
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 10298
 
8.2%
M 10211
 
8.1%
9664
 
7.7%
O 9191
 
7.3%
1 8952
 
7.1%
0 8355
 
6.7%
3 6022
 
4.8%
4 5451
 
4.3%
2 5128
 
4.1%
A 5092
 
4.1%
Other values (45) 47086
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 10298
 
8.2%
M 10211
 
8.1%
9664
 
7.7%
O 9191
 
7.3%
1 8952
 
7.1%
0 8355
 
6.7%
3 6022
 
4.8%
4 5451
 
4.3%
2 5128
 
4.1%
A 5092
 
4.1%
Other values (45) 47086
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 10298
 
8.2%
M 10211
 
8.1%
9664
 
7.7%
O 9191
 
7.3%
1 8952
 
7.1%
0 8355
 
6.7%
3 6022
 
4.8%
4 5451
 
4.3%
2 5128
 
4.1%
A 5092
 
4.1%
Other values (45) 47086
37.5%
Distinct18842
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:48.052398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length654
Median length580
Mean length69.48706668
Min length13

Characters and Unicode

Total characters1310943
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

Unique18818 ?
Unique (%)99.7%

Sample

1st rowMAM number 17903; female; personal specimen number MFH 162; testes 5 x 2 mm
2nd rowMAM number 17889; female
3rd rowMAM number 17897; male
4th rowMAM number 17895; male
5th rowMAM number 17888; female
ValueCountFrequency (%)
number 29739
 
16.2%
mam 18873
 
10.3%
original 6652
 
3.6%
catalog 6652
 
3.6%
male 5021
 
2.7%
osteo 4618
 
2.5%
specimen 4419
 
2.4%
personal 4201
 
2.3%
female 4185
 
2.3%
accn=ypm.12236 2399
 
1.3%
Other values (25895) 96369
52.6%
2025-02-28T12:47:48.267091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164262
 
12.5%
e 84610
 
6.5%
n 72932
 
5.6%
a 67392
 
5.1%
M 58097
 
4.4%
m 52492
 
4.0%
r 52119
 
4.0%
c 44063
 
3.4%
1 42870
 
3.3%
o 40086
 
3.1%
Other values (74) 632020
48.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1310943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
164262
 
12.5%
e 84610
 
6.5%
n 72932
 
5.6%
a 67392
 
5.1%
M 58097
 
4.4%
m 52492
 
4.0%
r 52119
 
4.0%
c 44063
 
3.4%
1 42870
 
3.3%
o 40086
 
3.1%
Other values (74) 632020
48.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1310943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
164262
 
12.5%
e 84610
 
6.5%
n 72932
 
5.6%
a 67392
 
5.1%
M 58097
 
4.4%
m 52492
 
4.0%
r 52119
 
4.0%
c 44063
 
3.4%
1 42870
 
3.3%
o 40086
 
3.1%
Other values (74) 632020
48.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1310943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
164262
 
12.5%
e 84610
 
6.5%
n 72932
 
5.6%
a 67392
 
5.1%
M 58097
 
4.4%
m 52492
 
4.0%
r 52119
 
4.0%
c 44063
 
3.4%
1 42870
 
3.3%
o 40086
 
3.1%
Other values (74) 632020
48.2%
Distinct3180
Distinct (%)17.0%
Missing152
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:48.390375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length135
Median length105
Mean length29.92679278
Min length3

Characters and Unicode

Total characters560050
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

Unique1569 ?
Unique (%)8.4%

Sample

1st rowTamias striatus fisheri
2nd rowPeromyscus leucopus noveboracensis
3rd rowPeromyscus leucopus noveboracensis
4th rowPeromyscus leucopus noveboracensis
5th rowPeromyscus leucopus noveboracensis
ValueCountFrequency (%)
peromyscus 1837
 
3.4%
gapperi 1530
 
2.8%
cinereus 1460
 
2.7%
brevicauda 1361
 
2.5%
sorex 1193
 
2.2%
blarina 976
 
1.8%
maniculatus 919
 
1.7%
zibethicus 906
 
1.7%
leucopus 836
 
1.6%
talpoides 759
 
1.4%
Other values (3630) 42002
78.1%
2025-02-28T12:47:48.591539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 54787
 
9.8%
i 49194
 
8.8%
a 47829
 
8.5%
u 39741
 
7.1%
e 39634
 
7.1%
r 35507
 
6.3%
35065
 
6.3%
o 33086
 
5.9%
n 28079
 
5.0%
c 25884
 
4.6%
Other values (45) 171244
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 560050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 54787
 
9.8%
i 49194
 
8.8%
a 47829
 
8.5%
u 39741
 
7.1%
e 39634
 
7.1%
r 35507
 
6.3%
35065
 
6.3%
o 33086
 
5.9%
n 28079
 
5.0%
c 25884
 
4.6%
Other values (45) 171244
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 560050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 54787
 
9.8%
i 49194
 
8.8%
a 47829
 
8.5%
u 39741
 
7.1%
e 39634
 
7.1%
r 35507
 
6.3%
35065
 
6.3%
o 33086
 
5.9%
n 28079
 
5.0%
c 25884
 
4.6%
Other values (45) 171244
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 560050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 54787
 
9.8%
i 49194
 
8.8%
a 47829
 
8.5%
u 39741
 
7.1%
e 39634
 
7.1%
r 35507
 
6.3%
35065
 
6.3%
o 33086
 
5.9%
n 28079
 
5.0%
c 25884
 
4.6%
Other values (45) 171244
30.6%

fieldNumber
Text

Missing 

Distinct5159
Distinct (%)70.6%
Missing11555
Missing (%)61.2%
Memory size147.5 KiB
2025-02-28T12:47:48.728997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length16
Mean length4.113664341
Min length1

Characters and Unicode

Total characters30075
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4249 ?
Unique (%)58.1%

Sample

1st row14251
2nd rowP5
3rd rowP14
4th rowP12
5th rowP4
ValueCountFrequency (%)
f 452
 
5.3%
r 169
 
2.0%
l 162
 
1.9%
mcz 50
 
0.6%
2 44
 
0.5%
3 43
 
0.5%
1 42
 
0.5%
5 38
 
0.4%
jas 32
 
0.4%
4 31
 
0.4%
Other values (4656) 7419
87.5%
2025-02-28T12:47:48.904292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4503
15.0%
3 2724
9.1%
4 2723
9.1%
2 2604
8.7%
0 2480
8.2%
8 2138
 
7.1%
9 1836
 
6.1%
7 1835
 
6.1%
5 1834
 
6.1%
6 1742
 
5.8%
Other values (58) 5656
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4503
15.0%
3 2724
9.1%
4 2723
9.1%
2 2604
8.7%
0 2480
8.2%
8 2138
 
7.1%
9 1836
 
6.1%
7 1835
 
6.1%
5 1834
 
6.1%
6 1742
 
5.8%
Other values (58) 5656
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4503
15.0%
3 2724
9.1%
4 2723
9.1%
2 2604
8.7%
0 2480
8.2%
8 2138
 
7.1%
9 1836
 
6.1%
7 1835
 
6.1%
5 1834
 
6.1%
6 1742
 
5.8%
Other values (58) 5656
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4503
15.0%
3 2724
9.1%
4 2723
9.1%
2 2604
8.7%
0 2480
8.2%
8 2138
 
7.1%
9 1836
 
6.1%
7 1835
 
6.1%
5 1834
 
6.1%
6 1742
 
5.8%
Other values (58) 5656
18.8%

eventDate
Text

Missing 

Distinct3892
Distinct (%)30.8%
Missing6221
Missing (%)33.0%
Memory size147.5 KiB
2025-02-28T12:47:49.026880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length10
Mean length9.587742191
Min length4

Characters and Unicode

Total characters121237
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

Unique2081 ?
Unique (%)16.5%

Sample

1st row2024-08-15
2nd row2023-12-01
3rd row2023-12-28
4th row2023-12-20
5th row2023-11-30
ValueCountFrequency (%)
2012-07-18 178
 
1.4%
2012-07-15 170
 
1.3%
1959 162
 
1.3%
1970/1973 156
 
1.2%
2012-07-16 150
 
1.2%
2012-07-24 144
 
1.1%
2013-08-02 109
 
0.9%
2020-10-07 108
 
0.9%
2020-10-14 100
 
0.8%
2020-10-08 96
 
0.8%
Other values (3882) 11272
89.1%
2025-02-28T12:47:49.222845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 22749
18.8%
0 22201
18.3%
1 21441
17.7%
2 12804
10.6%
9 11089
9.1%
7 6180
 
5.1%
6 5838
 
4.8%
5 5285
 
4.4%
3 4816
 
4.0%
8 4771
 
3.9%
Other values (2) 4063
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 22749
18.8%
0 22201
18.3%
1 21441
17.7%
2 12804
10.6%
9 11089
9.1%
7 6180
 
5.1%
6 5838
 
4.8%
5 5285
 
4.4%
3 4816
 
4.0%
8 4771
 
3.9%
Other values (2) 4063
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 22749
18.8%
0 22201
18.3%
1 21441
17.7%
2 12804
10.6%
9 11089
9.1%
7 6180
 
5.1%
6 5838
 
4.8%
5 5285
 
4.4%
3 4816
 
4.0%
8 4771
 
3.9%
Other values (2) 4063
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 22749
18.8%
0 22201
18.3%
1 21441
17.7%
2 12804
10.6%
9 11089
9.1%
7 6180
 
5.1%
6 5838
 
4.8%
5 5285
 
4.4%
3 4816
 
4.0%
8 4771
 
3.9%
Other values (2) 4063
 
3.4%

year
Text

Missing 

Distinct157
Distinct (%)1.2%
Missing6267
Missing (%)33.2%
Memory size147.5 KiB
2025-02-28T12:47:49.340125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters50396
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row2024
2nd row2023
3rd row2023
4th row2023
5th row2023
ValueCountFrequency (%)
2013 864
 
6.9%
2012 821
 
6.5%
2020 800
 
6.3%
2014 728
 
5.8%
1965 714
 
5.7%
1962 345
 
2.7%
1956 329
 
2.6%
1964 295
 
2.3%
1959 285
 
2.3%
1952 274
 
2.2%
Other values (147) 7144
56.7%
2025-02-28T12:47:49.494427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12085
24.0%
9 8638
17.1%
2 7713
15.3%
0 6850
13.6%
5 3400
 
6.7%
6 3380
 
6.7%
3 2762
 
5.5%
7 2062
 
4.1%
4 1835
 
3.6%
8 1671
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12085
24.0%
9 8638
17.1%
2 7713
15.3%
0 6850
13.6%
5 3400
 
6.7%
6 3380
 
6.7%
3 2762
 
5.5%
7 2062
 
4.1%
4 1835
 
3.6%
8 1671
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12085
24.0%
9 8638
17.1%
2 7713
15.3%
0 6850
13.6%
5 3400
 
6.7%
6 3380
 
6.7%
3 2762
 
5.5%
7 2062
 
4.1%
4 1835
 
3.6%
8 1671
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12085
24.0%
9 8638
17.1%
2 7713
15.3%
0 6850
13.6%
5 3400
 
6.7%
6 3380
 
6.7%
3 2762
 
5.5%
7 2062
 
4.1%
4 1835
 
3.6%
8 1671
 
3.3%

month
Text

Missing 

Distinct12
Distinct (%)0.1%
Missing7343
Missing (%)38.9%
Memory size147.5 KiB
2025-02-28T12:47:49.534291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.202638202
Min length1

Characters and Unicode

Total characters13858
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 row12
3rd row12
4th row12
5th row11
ValueCountFrequency (%)
7 2664
23.1%
8 1680
14.6%
10 1318
11.4%
6 1186
10.3%
9 830
 
7.2%
1 720
 
6.2%
11 609
 
5.3%
5 592
 
5.1%
4 512
 
4.4%
3 509
 
4.4%
Other values (2) 903
 
7.8%
2025-02-28T12:47:49.621804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3664
26.4%
7 2664
19.2%
8 1680
12.1%
0 1318
 
9.5%
6 1186
 
8.6%
2 903
 
6.5%
9 830
 
6.0%
5 592
 
4.3%
4 512
 
3.7%
3 509
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3664
26.4%
7 2664
19.2%
8 1680
12.1%
0 1318
 
9.5%
6 1186
 
8.6%
2 903
 
6.5%
9 830
 
6.0%
5 592
 
4.3%
4 512
 
3.7%
3 509
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3664
26.4%
7 2664
19.2%
8 1680
12.1%
0 1318
 
9.5%
6 1186
 
8.6%
2 903
 
6.5%
9 830
 
6.0%
5 592
 
4.3%
4 512
 
3.7%
3 509
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3664
26.4%
7 2664
19.2%
8 1680
12.1%
0 1318
 
9.5%
6 1186
 
8.6%
2 903
 
6.5%
9 830
 
6.0%
5 592
 
4.3%
4 512
 
3.7%
3 509
 
3.7%

day
Text

Missing 

Distinct31
Distinct (%)0.3%
Missing7899
Missing (%)41.9%
Memory size147.5 KiB
2025-02-28T12:47:49.667341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.677122276
Min length1

Characters and Unicode

Total characters18393
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 row15
2nd row1
3rd row28
4th row20
5th row30
ValueCountFrequency (%)
18 555
 
5.1%
15 518
 
4.7%
7 470
 
4.3%
16 465
 
4.2%
8 445
 
4.1%
9 434
 
4.0%
2 429
 
3.9%
24 429
 
3.9%
19 410
 
3.7%
4 386
 
3.5%
Other values (21) 6426
58.6%
2025-02-28T12:47:49.767459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5094
27.7%
2 4015
21.8%
3 1363
 
7.4%
8 1242
 
6.8%
4 1181
 
6.4%
7 1155
 
6.3%
5 1152
 
6.3%
6 1112
 
6.0%
9 1087
 
5.9%
0 992
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5094
27.7%
2 4015
21.8%
3 1363
 
7.4%
8 1242
 
6.8%
4 1181
 
6.4%
7 1155
 
6.3%
5 1152
 
6.3%
6 1112
 
6.0%
9 1087
 
5.9%
0 992
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5094
27.7%
2 4015
21.8%
3 1363
 
7.4%
8 1242
 
6.8%
4 1181
 
6.4%
7 1155
 
6.3%
5 1152
 
6.3%
6 1112
 
6.0%
9 1087
 
5.9%
0 992
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5094
27.7%
2 4015
21.8%
3 1363
 
7.4%
8 1242
 
6.8%
4 1181
 
6.4%
7 1155
 
6.3%
5 1152
 
6.3%
6 1112
 
6.0%
9 1087
 
5.9%
0 992
 
5.4%

habitat
Text

Missing 

Distinct49
Distinct (%)38.6%
Missing18739
Missing (%)99.3%
Memory size147.5 KiB
2025-02-28T12:47:49.814399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length185
Median length88
Mean length16.97637795
Min length5

Characters and Unicode

Total characters2156
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

Unique38 ?
Unique (%)29.9%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban
ValueCountFrequency (%)
urban 50
 
14.2%
in 21
 
5.9%
suburban 18
 
5.1%
forest 10
 
2.8%
by 8
 
2.3%
pine 7
 
2.0%
open 6
 
1.7%
of 6
 
1.7%
ponderosa 6
 
1.7%
soil 5
 
1.4%
Other values (132) 216
61.2%
2025-02-28T12:47:49.935864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
226
 
10.5%
a 205
 
9.5%
n 189
 
8.8%
r 178
 
8.3%
e 162
 
7.5%
o 131
 
6.1%
s 119
 
5.5%
b 116
 
5.4%
i 105
 
4.9%
t 98
 
4.5%
Other values (36) 627
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
226
 
10.5%
a 205
 
9.5%
n 189
 
8.8%
r 178
 
8.3%
e 162
 
7.5%
o 131
 
6.1%
s 119
 
5.5%
b 116
 
5.4%
i 105
 
4.9%
t 98
 
4.5%
Other values (36) 627
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
226
 
10.5%
a 205
 
9.5%
n 189
 
8.8%
r 178
 
8.3%
e 162
 
7.5%
o 131
 
6.1%
s 119
 
5.5%
b 116
 
5.4%
i 105
 
4.9%
t 98
 
4.5%
Other values (36) 627
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
226
 
10.5%
a 205
 
9.5%
n 189
 
8.8%
r 178
 
8.3%
e 162
 
7.5%
o 131
 
6.1%
s 119
 
5.5%
b 116
 
5.4%
i 105
 
4.9%
t 98
 
4.5%
Other values (36) 627
29.1%

higherGeography
Text

Missing 

Distinct951
Distinct (%)6.3%
Missing3778
Missing (%)20.0%
Memory size147.5 KiB
2025-02-28T12:47:50.071071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length74
Median length66
Mean length40.53313892
Min length4

Characters and Unicode

Total characters611564
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)2.1%

Sample

1st rowNorth America; USA; Connecticut; New Haven County
2nd rowNorth America; USA; Connecticut; Middlesex County
3rd rowNorth America; USA; Connecticut; Middlesex County
4th rowNorth America; USA; Connecticut; Middlesex County
5th rowNorth America; USA; Connecticut; Middlesex County
ValueCountFrequency (%)
america 11919
14.1%
north 11535
13.6%
usa 10091
 
11.9%
county 9449
 
11.1%
new 4323
 
5.1%
hampshire 2881
 
3.4%
carroll 2750
 
3.2%
africa 2011
 
2.4%
connecticut 1497
 
1.8%
province 1319
 
1.6%
Other values (974) 27017
31.9%
2025-02-28T12:47:50.286936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69704
 
11.4%
r 45359
 
7.4%
a 42864
 
7.0%
o 40987
 
6.7%
; 38761
 
6.3%
e 35621
 
5.8%
i 34064
 
5.6%
t 32303
 
5.3%
n 28583
 
4.7%
A 26232
 
4.3%
Other values (53) 217086
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 611564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
69704
 
11.4%
r 45359
 
7.4%
a 42864
 
7.0%
o 40987
 
6.7%
; 38761
 
6.3%
e 35621
 
5.8%
i 34064
 
5.6%
t 32303
 
5.3%
n 28583
 
4.7%
A 26232
 
4.3%
Other values (53) 217086
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 611564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
69704
 
11.4%
r 45359
 
7.4%
a 42864
 
7.0%
o 40987
 
6.7%
; 38761
 
6.3%
e 35621
 
5.8%
i 34064
 
5.6%
t 32303
 
5.3%
n 28583
 
4.7%
A 26232
 
4.3%
Other values (53) 217086
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 611564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
69704
 
11.4%
r 45359
 
7.4%
a 42864
 
7.0%
o 40987
 
6.7%
; 38761
 
6.3%
e 35621
 
5.8%
i 34064
 
5.6%
t 32303
 
5.3%
n 28583
 
4.7%
A 26232
 
4.3%
Other values (53) 217086
35.5%

continent
Text

Missing 

Distinct6
Distinct (%)< 0.1%
Missing3913
Missing (%)20.7%
Memory size147.5 KiB
2025-02-28T12:47:50.323616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.50270849
Min length4

Characters and Unicode

Total characters172000
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth America
2nd rowNorth America
3rd rowNorth America
4th rowNorth America
5th rowNorth America
ValueCountFrequency (%)
america 11919
44.4%
north 11399
42.4%
africa 1984
 
7.4%
asia 640
 
2.4%
south 520
 
1.9%
europe 281
 
1.0%
oceania 129
 
0.5%
2025-02-28T12:47:50.490352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 25583
14.9%
a 14801
8.6%
i 14672
8.5%
A 14543
8.5%
c 14032
8.2%
e 12329
7.2%
o 12200
7.1%
t 11919
6.9%
h 11919
6.9%
11919
6.9%
Other values (10) 28083
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 172000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 25583
14.9%
a 14801
8.6%
i 14672
8.5%
A 14543
8.5%
c 14032
8.2%
e 12329
7.2%
o 12200
7.1%
t 11919
6.9%
h 11919
6.9%
11919
6.9%
Other values (10) 28083
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 172000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 25583
14.9%
a 14801
8.6%
i 14672
8.5%
A 14543
8.5%
c 14032
8.2%
e 12329
7.2%
o 12200
7.1%
t 11919
6.9%
h 11919
6.9%
11919
6.9%
Other values (10) 28083
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 172000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 25583
14.9%
a 14801
8.6%
i 14672
8.5%
A 14543
8.5%
c 14032
8.2%
e 12329
7.2%
o 12200
7.1%
t 11919
6.9%
h 11919
6.9%
11919
6.9%
Other values (10) 28083
16.3%

waterBody
Text

Missing 

Distinct7
Distinct (%)5.5%
Missing18739
Missing (%)99.3%
Memory size147.5 KiB
2025-02-28T12:47:50.520033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length29
Mean length23.07874016
Min length12

Characters and Unicode

Total characters2931
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

Unique3 ?
Unique (%)2.4%

Sample

1st rowAtlantic Ocean; Caribbean Sea
2nd rowAtlantic Ocean; Caribbean Sea
3rd rowAtlantic Ocean; Caribbean Sea
4th rowAtlantic Ocean; Caribbean Sea
5th rowAtlantic Ocean; Caribbean Sea
ValueCountFrequency (%)
ocean 127
30.5%
atlantic 87
20.9%
sea 79
19.0%
caribbean 78
18.8%
pacific 30
 
7.2%
indian 9
 
2.2%
arctic 1
 
0.2%
red 1
 
0.2%
gulf 1
 
0.2%
of 1
 
0.2%
Other values (2) 2
 
0.5%
2025-02-28T12:47:50.598094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 490
16.7%
n 312
10.6%
289
9.9%
e 287
9.8%
c 277
9.5%
i 236
8.1%
t 176
 
6.0%
b 156
 
5.3%
O 127
 
4.3%
A 88
 
3.0%
Other values (15) 493
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 490
16.7%
n 312
10.6%
289
9.9%
e 287
9.8%
c 277
9.5%
i 236
8.1%
t 176
 
6.0%
b 156
 
5.3%
O 127
 
4.3%
A 88
 
3.0%
Other values (15) 493
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 490
16.7%
n 312
10.6%
289
9.9%
e 287
9.8%
c 277
9.5%
i 236
8.1%
t 176
 
6.0%
b 156
 
5.3%
O 127
 
4.3%
A 88
 
3.0%
Other values (15) 493
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 490
16.7%
n 312
10.6%
289
9.9%
e 287
9.8%
c 277
9.5%
i 236
8.1%
t 176
 
6.0%
b 156
 
5.3%
O 127
 
4.3%
A 88
 
3.0%
Other values (15) 493
16.8%

country
Text

Missing 

Distinct105
Distinct (%)0.7%
Missing3927
Missing (%)20.8%
Memory size147.5 KiB
2025-02-28T12:47:50.630389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length3
Mean length4.21547627
Min length3

Characters and Unicode

Total characters62975
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

Unique17 ?
Unique (%)0.1%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
usa 10091
66.2%
canada 686
 
4.5%
kenya 667
 
4.4%
mexico 578
 
3.8%
egypt 430
 
2.8%
indonesia 279
 
1.8%
cameroon 254
 
1.7%
ecuador 236
 
1.5%
greece 138
 
0.9%
australia 112
 
0.7%
Other values (112) 1762
 
11.6%
2025-02-28T12:47:50.717288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10274
16.3%
S 10260
16.3%
U 10116
16.1%
a 5889
9.4%
n 3063
 
4.9%
e 2749
 
4.4%
i 2122
 
3.4%
o 2093
 
3.3%
d 1537
 
2.4%
r 1381
 
2.2%
Other values (38) 13491
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10274
16.3%
S 10260
16.3%
U 10116
16.1%
a 5889
9.4%
n 3063
 
4.9%
e 2749
 
4.4%
i 2122
 
3.4%
o 2093
 
3.3%
d 1537
 
2.4%
r 1381
 
2.2%
Other values (38) 13491
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10274
16.3%
S 10260
16.3%
U 10116
16.1%
a 5889
9.4%
n 3063
 
4.9%
e 2749
 
4.4%
i 2122
 
3.4%
o 2093
 
3.3%
d 1537
 
2.4%
r 1381
 
2.2%
Other values (38) 13491
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10274
16.3%
S 10260
16.3%
U 10116
16.1%
a 5889
9.4%
n 3063
 
4.9%
e 2749
 
4.4%
i 2122
 
3.4%
o 2093
 
3.3%
d 1537
 
2.4%
r 1381
 
2.2%
Other values (38) 13491
21.4%

stateProvince
Text

Missing 

Distinct260
Distinct (%)1.9%
Missing5347
Missing (%)28.3%
Memory size147.5 KiB
2025-02-28T12:47:50.844032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length25
Mean length11.24032843
Min length3

Characters and Unicode

Total characters151958
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

Unique72 ?
Unique (%)0.5%

Sample

1st rowConnecticut
2nd rowConnecticut
3rd rowConnecticut
4th rowConnecticut
5th rowConnecticut
ValueCountFrequency (%)
new 3586
17.2%
hampshire 2877
 
13.8%
connecticut 1497
 
7.2%
province 1288
 
6.2%
state 613
 
2.9%
minnesota 580
 
2.8%
york 506
 
2.4%
colorado 463
 
2.2%
arizona 438
 
2.1%
wisconsin 425
 
2.0%
Other values (287) 8636
41.3%
2025-02-28T12:47:51.039350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14301
 
9.4%
a 13747
 
9.0%
i 12892
 
8.5%
n 10792
 
7.1%
o 10626
 
7.0%
r 9355
 
6.2%
t 8029
 
5.3%
s 7835
 
5.2%
7390
 
4.9%
c 5643
 
3.7%
Other values (48) 51348
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14301
 
9.4%
a 13747
 
9.0%
i 12892
 
8.5%
n 10792
 
7.1%
o 10626
 
7.0%
r 9355
 
6.2%
t 8029
 
5.3%
s 7835
 
5.2%
7390
 
4.9%
c 5643
 
3.7%
Other values (48) 51348
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14301
 
9.4%
a 13747
 
9.0%
i 12892
 
8.5%
n 10792
 
7.1%
o 10626
 
7.0%
r 9355
 
6.2%
t 8029
 
5.3%
s 7835
 
5.2%
7390
 
4.9%
c 5643
 
3.7%
Other values (48) 51348
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14301
 
9.4%
a 13747
 
9.0%
i 12892
 
8.5%
n 10792
 
7.1%
o 10626
 
7.0%
r 9355
 
6.2%
t 8029
 
5.3%
s 7835
 
5.2%
7390
 
4.9%
c 5643
 
3.7%
Other values (48) 51348
33.8%

county
Text

Missing 

Distinct484
Distinct (%)5.0%
Missing9192
Missing (%)48.7%
Memory size147.5 KiB
2025-02-28T12:47:51.178720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length27
Mean length14.43198263
Min length6

Characters and Unicode

Total characters139615
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)1.6%

Sample

1st rowNew Haven County
2nd rowMiddlesex County
3rd rowMiddlesex County
4th rowMiddlesex County
5th rowMiddlesex County
ValueCountFrequency (%)
county 9433
45.6%
carroll 2750
 
13.3%
new 705
 
3.4%
haven 655
 
3.2%
cass 356
 
1.7%
litchfield 334
 
1.6%
gunnison 275
 
1.3%
fairfield 220
 
1.1%
iron 203
 
1.0%
middlesex 167
 
0.8%
Other values (517) 5606
27.1%
2025-02-28T12:47:51.372527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 15785
11.3%
n 14029
10.0%
C 13107
9.4%
t 11232
 
8.0%
11030
 
7.9%
u 10798
 
7.7%
y 9773
 
7.0%
r 8601
 
6.2%
l 7999
 
5.7%
a 7541
 
5.4%
Other values (47) 29720
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 15785
11.3%
n 14029
10.0%
C 13107
9.4%
t 11232
 
8.0%
11030
 
7.9%
u 10798
 
7.7%
y 9773
 
7.0%
r 8601
 
6.2%
l 7999
 
5.7%
a 7541
 
5.4%
Other values (47) 29720
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 15785
11.3%
n 14029
10.0%
C 13107
9.4%
t 11232
 
8.0%
11030
 
7.9%
u 10798
 
7.7%
y 9773
 
7.0%
r 8601
 
6.2%
l 7999
 
5.7%
a 7541
 
5.4%
Other values (47) 29720
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 15785
11.3%
n 14029
10.0%
C 13107
9.4%
t 11232
 
8.0%
11030
 
7.9%
u 10798
 
7.7%
y 9773
 
7.0%
r 8601
 
6.2%
l 7999
 
5.7%
a 7541
 
5.4%
Other values (47) 29720
21.3%

municipality
Text

Missing 

Distinct93
Distinct (%)16.7%
Missing18309
Missing (%)97.0%
Memory size147.5 KiB
2025-02-28T12:47:51.429039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length19
Mean length8.47935368
Min length4

Characters and Unicode

Total characters4723
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)6.6%

Sample

1st rowRedding
2nd rowHamden
3rd rowHamden
4th rowPerkasie
5th rowPhiladelphia
ValueCountFrequency (%)
parksville 56
 
8.5%
fairfield 39
 
5.9%
westport 35
 
5.3%
kent 32
 
4.9%
norwalk 29
 
4.4%
lloyd 27
 
4.1%
harbor 27
 
4.1%
new 25
 
3.8%
quince 24
 
3.6%
mil 24
 
3.6%
Other values (98) 340
51.7%
2025-02-28T12:47:51.534047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 421
 
8.9%
e 410
 
8.7%
a 396
 
8.4%
r 359
 
7.6%
i 356
 
7.5%
o 282
 
6.0%
n 258
 
5.5%
t 205
 
4.3%
s 184
 
3.9%
d 163
 
3.5%
Other values (39) 1689
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 421
 
8.9%
e 410
 
8.7%
a 396
 
8.4%
r 359
 
7.6%
i 356
 
7.5%
o 282
 
6.0%
n 258
 
5.5%
t 205
 
4.3%
s 184
 
3.9%
d 163
 
3.5%
Other values (39) 1689
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 421
 
8.9%
e 410
 
8.7%
a 396
 
8.4%
r 359
 
7.6%
i 356
 
7.5%
o 282
 
6.0%
n 258
 
5.5%
t 205
 
4.3%
s 184
 
3.9%
d 163
 
3.5%
Other values (39) 1689
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 421
 
8.9%
e 410
 
8.7%
a 396
 
8.4%
r 359
 
7.6%
i 356
 
7.5%
o 282
 
6.0%
n 258
 
5.5%
t 205
 
4.3%
s 184
 
3.9%
d 163
 
3.5%
Other values (39) 1689
35.8%

locality
Text

Missing 

Distinct2520
Distinct (%)19.4%
Missing5869
Missing (%)31.1%
Memory size147.5 KiB
2025-02-28T12:47:51.663543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length96
Mean length26.34000154
Min length3

Characters and Unicode

Total characters342341
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1275 ?
Unique (%)9.8%

Sample

1st rowNew Haven. Yale University, Peabody Museum
2nd rowClinton. 245 Killingworth Turnpike
3rd rowClinton. 245 Killingworth Turnpike
4th rowClinton. 245 Killingworth Turnpike
5th rowClinton. 245 Killingworth Turnpike
ValueCountFrequency (%)
forest 3560
 
6.6%
experimental 2766
 
5.1%
bartlett 2744
 
5.1%
of 2288
 
4.2%
comp 1856
 
3.4%
miles 929
 
1.7%
transect 736
 
1.4%
mi 727
 
1.3%
national 657
 
1.2%
island 533
 
1.0%
Other values (3204) 37538
69.1%
2025-02-28T12:47:51.875278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41355
 
12.1%
e 29260
 
8.5%
a 26473
 
7.7%
t 25066
 
7.3%
o 21028
 
6.1%
r 19414
 
5.7%
n 17045
 
5.0%
i 15970
 
4.7%
l 15843
 
4.6%
s 12163
 
3.6%
Other values (76) 118724
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 342341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41355
 
12.1%
e 29260
 
8.5%
a 26473
 
7.7%
t 25066
 
7.3%
o 21028
 
6.1%
r 19414
 
5.7%
n 17045
 
5.0%
i 15970
 
4.7%
l 15843
 
4.6%
s 12163
 
3.6%
Other values (76) 118724
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 342341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41355
 
12.1%
e 29260
 
8.5%
a 26473
 
7.7%
t 25066
 
7.3%
o 21028
 
6.1%
r 19414
 
5.7%
n 17045
 
5.0%
i 15970
 
4.7%
l 15843
 
4.6%
s 12163
 
3.6%
Other values (76) 118724
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 342341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41355
 
12.1%
e 29260
 
8.5%
a 26473
 
7.7%
t 25066
 
7.3%
o 21028
 
6.1%
r 19414
 
5.7%
n 17045
 
5.0%
i 15970
 
4.7%
l 15843
 
4.6%
s 12163
 
3.6%
Other values (76) 118724
34.7%
Distinct155
Distinct (%)10.5%
Missing17391
Missing (%)92.2%
Memory size147.5 KiB
2025-02-28T12:47:51.949923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.444067797
Min length1

Characters and Unicode

Total characters5080
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

Unique39 ?
Unique (%)2.6%

Sample

1st row61
2nd row61
3rd row638
4th row638
5th row1143
ValueCountFrequency (%)
1829 124
 
8.4%
61 104
 
7.1%
2896 60
 
4.1%
2134 59
 
4.0%
700 59
 
4.0%
638 56
 
3.8%
1000 53
 
3.6%
500 42
 
2.8%
1402 29
 
2.0%
1280 29
 
2.0%
Other values (145) 860
58.3%
2025-02-28T12:47:52.077111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 934
18.4%
0 927
18.2%
2 635
12.5%
8 506
10.0%
6 441
8.7%
9 373
 
7.3%
3 369
 
7.3%
7 307
 
6.0%
4 294
 
5.8%
5 294
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 934
18.4%
0 927
18.2%
2 635
12.5%
8 506
10.0%
6 441
8.7%
9 373
 
7.3%
3 369
 
7.3%
7 307
 
6.0%
4 294
 
5.8%
5 294
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 934
18.4%
0 927
18.2%
2 635
12.5%
8 506
10.0%
6 441
8.7%
9 373
 
7.3%
3 369
 
7.3%
7 307
 
6.0%
4 294
 
5.8%
5 294
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 934
18.4%
0 927
18.2%
2 635
12.5%
8 506
10.0%
6 441
8.7%
9 373
 
7.3%
3 369
 
7.3%
7 307
 
6.0%
4 294
 
5.8%
5 294
 
5.8%
Distinct110
Distinct (%)14.0%
Missing18082
Missing (%)95.8%
Memory size147.5 KiB
2025-02-28T12:47:52.137366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.341836735
Min length1

Characters and Unicode

Total characters2620
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

Unique29 ?
Unique (%)3.7%

Sample

1st row61
2nd row61
3rd row61
4th row1829
5th row61
ValueCountFrequency (%)
61 104
 
13.3%
1829 42
 
5.4%
1000 31
 
4.0%
1402 29
 
3.7%
1280 28
 
3.6%
2896 27
 
3.4%
91 27
 
3.4%
30 24
 
3.1%
2743 23
 
2.9%
1585 18
 
2.3%
Other values (100) 431
55.0%
2025-02-28T12:47:52.254100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 597
22.8%
0 472
18.0%
2 332
12.7%
6 250
9.5%
8 204
 
7.8%
9 192
 
7.3%
5 186
 
7.1%
3 145
 
5.5%
4 134
 
5.1%
7 108
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 597
22.8%
0 472
18.0%
2 332
12.7%
6 250
9.5%
8 204
 
7.8%
9 192
 
7.3%
5 186
 
7.1%
3 145
 
5.5%
4 134
 
5.1%
7 108
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 597
22.8%
0 472
18.0%
2 332
12.7%
6 250
9.5%
8 204
 
7.8%
9 192
 
7.3%
5 186
 
7.1%
3 145
 
5.5%
4 134
 
5.1%
7 108
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 597
22.8%
0 472
18.0%
2 332
12.7%
6 250
9.5%
8 204
 
7.8%
9 192
 
7.3%
5 186
 
7.1%
3 145
 
5.5%
4 134
 
5.1%
7 108
 
4.1%

verbatimElevation
Text

Missing 

Distinct195
Distinct (%)13.2%
Missing17391
Missing (%)92.2%
Memory size147.5 KiB
2025-02-28T12:47:52.286601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.446779661
Min length4

Characters and Unicode

Total characters12459
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

Unique57 ?
Unique (%)3.9%

Sample

1st row200-200 ft
2nd row200-200 ft
3rd row638 m
4th row638 m
5th row1143 m
ValueCountFrequency (%)
m 858
29.1%
ft 617
20.9%
200-200 104
 
3.5%
1829 84
 
2.8%
700 58
 
2.0%
638 56
 
1.9%
2134 56
 
1.9%
6000-6000 40
 
1.4%
500 39
 
1.3%
2896 33
 
1.1%
Other values (172) 1005
34.1%
2025-02-28T12:47:52.369722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3601
28.9%
1475
11.8%
m 858
 
6.9%
- 784
 
6.3%
2 739
 
5.9%
1 683
 
5.5%
f 617
 
5.0%
t 617
 
5.0%
8 490
 
3.9%
4 490
 
3.9%
Other values (5) 2105
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3601
28.9%
1475
11.8%
m 858
 
6.9%
- 784
 
6.3%
2 739
 
5.9%
1 683
 
5.5%
f 617
 
5.0%
t 617
 
5.0%
8 490
 
3.9%
4 490
 
3.9%
Other values (5) 2105
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3601
28.9%
1475
11.8%
m 858
 
6.9%
- 784
 
6.3%
2 739
 
5.9%
1 683
 
5.5%
f 617
 
5.0%
t 617
 
5.0%
8 490
 
3.9%
4 490
 
3.9%
Other values (5) 2105
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3601
28.9%
1475
11.8%
m 858
 
6.9%
- 784
 
6.3%
2 739
 
5.9%
1 683
 
5.5%
f 617
 
5.0%
t 617
 
5.0%
8 490
 
3.9%
4 490
 
3.9%
Other values (5) 2105
16.9%

decimalLatitude
Text

Missing 

Distinct2258
Distinct (%)16.9%
Missing5543
Missing (%)29.4%
Memory size147.5 KiB
2025-02-28T12:47:52.503962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.821736846
Min length1

Characters and Unicode

Total characters104209
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

Unique1064 ?
Unique (%)8.0%

Sample

1st row41.358888859069
2nd row41.358888859069
3rd row40.2804715
4th row39.9660548
5th row40.2804715
ValueCountFrequency (%)
44.049466 311
 
2.3%
44.059277 252
 
1.9%
44.062155 245
 
1.8%
3.9167 244
 
1.8%
44.061185 228
 
1.7%
44.041766 222
 
1.7%
44.059944 204
 
1.5%
44.050880 202
 
1.5%
41.3931 147
 
1.1%
41.3081 130
 
1.0%
Other values (2222) 11138
83.6%
2025-02-28T12:47:52.715954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 15858
15.2%
. 13291
12.8%
3 11118
10.7%
0 11016
10.6%
1 8934
8.6%
6 8422
8.1%
5 7759
7.4%
7 6947
6.7%
2 6946
6.7%
8 6900
6.6%
Other values (2) 7018
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 15858
15.2%
. 13291
12.8%
3 11118
10.7%
0 11016
10.6%
1 8934
8.6%
6 8422
8.1%
5 7759
7.4%
7 6947
6.7%
2 6946
6.7%
8 6900
6.6%
Other values (2) 7018
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 15858
15.2%
. 13291
12.8%
3 11118
10.7%
0 11016
10.6%
1 8934
8.6%
6 8422
8.1%
5 7759
7.4%
7 6947
6.7%
2 6946
6.7%
8 6900
6.6%
Other values (2) 7018
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 15858
15.2%
. 13291
12.8%
3 11118
10.7%
0 11016
10.6%
1 8934
8.6%
6 8422
8.1%
5 7759
7.4%
7 6947
6.7%
2 6946
6.7%
8 6900
6.6%
Other values (2) 7018
6.7%

decimalLongitude
Text

Missing 

Distinct2296
Distinct (%)17.2%
Missing5543
Missing (%)29.4%
Memory size147.5 KiB
2025-02-28T12:47:52.862453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length11
Mean length8.931772123
Min length1

Characters and Unicode

Total characters118998
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

Unique1103 ?
Unique (%)8.3%

Sample

1st row-72.90380733456
2nd row-72.90380733456
3rd row-75.0506836
4th row-75.1956828
5th row-75.0506836
ValueCountFrequency (%)
71.273830 311
 
2.3%
71.304611 252
 
1.9%
71.297795 245
 
1.8%
136.1667 244
 
1.8%
71.307927 232
 
1.7%
71.303074 228
 
1.7%
71.319924 222
 
1.7%
71.308122 204
 
1.5%
71.2903479 160
 
1.2%
72.8972 148
 
1.1%
Other values (2281) 11077
83.1%
2025-02-28T12:47:53.070225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14237
12.0%
7 13643
11.5%
. 13252
11.1%
3 11911
10.0%
0 11591
9.7%
- 11234
9.4%
2 8871
7.5%
6 8250
6.9%
9 7707
6.5%
8 7413
6.2%
Other values (2) 10889
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14237
12.0%
7 13643
11.5%
. 13252
11.1%
3 11911
10.0%
0 11591
9.7%
- 11234
9.4%
2 8871
7.5%
6 8250
6.9%
9 7707
6.5%
8 7413
6.2%
Other values (2) 10889
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14237
12.0%
7 13643
11.5%
. 13252
11.1%
3 11911
10.0%
0 11591
9.7%
- 11234
9.4%
2 8871
7.5%
6 8250
6.9%
9 7707
6.5%
8 7413
6.2%
Other values (2) 10889
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14237
12.0%
7 13643
11.5%
. 13252
11.1%
3 11911
10.0%
0 11591
9.7%
- 11234
9.4%
2 8871
7.5%
6 8250
6.9%
9 7707
6.5%
8 7413
6.2%
Other values (2) 10889
9.2%

geodeticDatum
Text

Missing 

Distinct2
Distinct (%)< 0.1%
Missing5666
Missing (%)30.0%
Memory size147.5 KiB
2025-02-28T12:47:53.120013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters66000
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 rowWGS84
2nd rowWGS84
3rd rowWGS84
4th rowWGS84
5th rowWGS84
ValueCountFrequency (%)
wgs84 12952
98.1%
nad27 248
 
1.9%
2025-02-28T12:47:53.200644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 12952
19.6%
G 12952
19.6%
S 12952
19.6%
8 12952
19.6%
4 12952
19.6%
N 248
 
0.4%
A 248
 
0.4%
D 248
 
0.4%
2 248
 
0.4%
7 248
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 12952
19.6%
G 12952
19.6%
S 12952
19.6%
8 12952
19.6%
4 12952
19.6%
N 248
 
0.4%
A 248
 
0.4%
D 248
 
0.4%
2 248
 
0.4%
7 248
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 12952
19.6%
G 12952
19.6%
S 12952
19.6%
8 12952
19.6%
4 12952
19.6%
N 248
 
0.4%
A 248
 
0.4%
D 248
 
0.4%
2 248
 
0.4%
7 248
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 12952
19.6%
G 12952
19.6%
S 12952
19.6%
8 12952
19.6%
4 12952
19.6%
N 248
 
0.4%
A 248
 
0.4%
D 248
 
0.4%
2 248
 
0.4%
7 248
 
0.4%
Distinct476
Distinct (%)3.6%
Missing5609
Missing (%)29.7%
Memory size147.5 KiB
2025-02-28T12:47:53.237283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.104246813
Min length2

Characters and Unicode

Total characters54410
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

Unique228 ?
Unique (%)1.7%

Sample

1st row5359
2nd row5359
3rd row5359
4th row5359
5th row5359
ValueCountFrequency (%)
1850 5476
41.3%
1851 4930
37.2%
111111 329
 
2.5%
3036 110
 
0.8%
1583 104
 
0.8%
301 96
 
0.7%
103733 86
 
0.6%
5000 84
 
0.6%
300 79
 
0.6%
500 66
 
0.5%
Other values (466) 1897
 
14.3%
2025-02-28T12:47:53.330114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19011
34.9%
5 11398
20.9%
8 11362
20.9%
0 7286
 
13.4%
3 1449
 
2.7%
4 978
 
1.8%
7 822
 
1.5%
6 746
 
1.4%
2 681
 
1.3%
9 673
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19011
34.9%
5 11398
20.9%
8 11362
20.9%
0 7286
 
13.4%
3 1449
 
2.7%
4 978
 
1.8%
7 822
 
1.5%
6 746
 
1.4%
2 681
 
1.3%
9 673
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19011
34.9%
5 11398
20.9%
8 11362
20.9%
0 7286
 
13.4%
3 1449
 
2.7%
4 978
 
1.8%
7 822
 
1.5%
6 746
 
1.4%
2 681
 
1.3%
9 673
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19011
34.9%
5 11398
20.9%
8 11362
20.9%
0 7286
 
13.4%
3 1449
 
2.7%
4 978
 
1.8%
7 822
 
1.5%
6 746
 
1.4%
2 681
 
1.3%
9 673
 
1.2%

georeferencedBy
Text

Missing 

Distinct14
Distinct (%)4.3%
Missing18537
Missing (%)98.3%
Memory size147.5 KiB
2025-02-28T12:47:53.369042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length17
Mean length17.73860182
Min length13

Characters and Unicode

Total characters5836
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

Unique5 ?
Unique (%)1.5%

Sample

1st rowPiper L. Stepule
2nd rowPiper L. Stepule
3rd rowPeter A. Capainolo
4th rowKristof Zyskowski
5th rowNicholas J. Kerhoulas
ValueCountFrequency (%)
kristof 233
31.6%
zyskowski 233
31.6%
j 37
 
5.0%
gregory 24
 
3.3%
watkins-colwell 24
 
3.3%
peter 22
 
3.0%
a 22
 
3.0%
capainolo 22
 
3.0%
dornburg 14
 
1.9%
alex 14
 
1.9%
Other values (26) 93
 
12.6%
2025-02-28T12:47:53.462718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 761
13.0%
o 607
 
10.4%
i 545
 
9.3%
k 497
 
8.5%
409
 
7.0%
r 364
 
6.2%
t 294
 
5.0%
y 269
 
4.6%
w 263
 
4.5%
K 251
 
4.3%
Other values (32) 1576
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 761
13.0%
o 607
 
10.4%
i 545
 
9.3%
k 497
 
8.5%
409
 
7.0%
r 364
 
6.2%
t 294
 
5.0%
y 269
 
4.6%
w 263
 
4.5%
K 251
 
4.3%
Other values (32) 1576
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 761
13.0%
o 607
 
10.4%
i 545
 
9.3%
k 497
 
8.5%
409
 
7.0%
r 364
 
6.2%
t 294
 
5.0%
y 269
 
4.6%
w 263
 
4.5%
K 251
 
4.3%
Other values (32) 1576
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 761
13.0%
o 607
 
10.4%
i 545
 
9.3%
k 497
 
8.5%
409
 
7.0%
r 364
 
6.2%
t 294
 
5.0%
y 269
 
4.6%
w 263
 
4.5%
K 251
 
4.3%
Other values (32) 1576
27.0%

georeferencedDate
Text

Missing 

Distinct48
Distinct (%)0.6%
Missing10549
Missing (%)55.9%
Memory size147.5 KiB
2025-02-28T12:47:53.490840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.131417578
Min length4

Characters and Unicode

Total characters75946
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

Unique16 ?
Unique (%)0.2%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015
ValueCountFrequency (%)
2023-12-28 5807
69.8%
2015 1204
 
14.5%
2020-06-14 935
 
11.2%
2020-12-30 124
 
1.5%
2023-12-03 45
 
0.5%
2021-12-08 27
 
0.3%
2024-01-17 18
 
0.2%
2024-05-01 17
 
0.2%
2019-11-04 16
 
0.2%
2022-06-18 14
 
0.2%
Other values (38) 110
 
1.3%
2025-02-28T12:47:53.654503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 27323
36.0%
- 14226
18.7%
0 10723
 
14.1%
1 8422
 
11.1%
3 6079
 
8.0%
8 5869
 
7.7%
5 1236
 
1.6%
4 1028
 
1.4%
6 994
 
1.3%
7 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 27323
36.0%
- 14226
18.7%
0 10723
 
14.1%
1 8422
 
11.1%
3 6079
 
8.0%
8 5869
 
7.7%
5 1236
 
1.6%
4 1028
 
1.4%
6 994
 
1.3%
7 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 27323
36.0%
- 14226
18.7%
0 10723
 
14.1%
1 8422
 
11.1%
3 6079
 
8.0%
8 5869
 
7.7%
5 1236
 
1.6%
4 1028
 
1.4%
6 994
 
1.3%
7 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 27323
36.0%
- 14226
18.7%
0 10723
 
14.1%
1 8422
 
11.1%
3 6079
 
8.0%
8 5869
 
7.7%
5 1236
 
1.6%
4 1028
 
1.4%
6 994
 
1.3%
7 24
 
< 0.1%

georeferenceProtocol
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing5610
Missing (%)29.7%
Memory size147.5 KiB
2025-02-28T12:47:53.683802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.75980688
Min length11

Characters and Unicode

Total characters182400
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdigital resource
2nd rowdigital resource
3rd rowdigital resource
4th rowdigital resource
5th rowdigital resource
ValueCountFrequency (%)
resource 7300
35.5%
digital 7216
35.1%
unspecified 5956
29.0%
physical 84
 
0.4%
2025-02-28T12:47:53.769790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 26512
14.5%
i 26428
14.5%
r 14600
 
8.0%
s 13340
 
7.3%
c 13340
 
7.3%
u 13256
 
7.3%
d 13172
 
7.2%
7300
 
4.0%
l 7300
 
4.0%
a 7300
 
4.0%
Other values (8) 39852
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 26512
14.5%
i 26428
14.5%
r 14600
 
8.0%
s 13340
 
7.3%
c 13340
 
7.3%
u 13256
 
7.3%
d 13172
 
7.2%
7300
 
4.0%
l 7300
 
4.0%
a 7300
 
4.0%
Other values (8) 39852
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 26512
14.5%
i 26428
14.5%
r 14600
 
8.0%
s 13340
 
7.3%
c 13340
 
7.3%
u 13256
 
7.3%
d 13172
 
7.2%
7300
 
4.0%
l 7300
 
4.0%
a 7300
 
4.0%
Other values (8) 39852
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 26512
14.5%
i 26428
14.5%
r 14600
 
8.0%
s 13340
 
7.3%
c 13340
 
7.3%
u 13256
 
7.3%
d 13172
 
7.2%
7300
 
4.0%
l 7300
 
4.0%
a 7300
 
4.0%
Other values (8) 39852
21.8%

georeferenceSources
Text

Missing 

Distinct14
Distinct (%)0.1%
Missing5615
Missing (%)29.8%
Memory size147.5 KiB
2025-02-28T12:47:53.801200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length15
Mean length9.898347295
Min length4

Characters and Unicode

Total characters131163
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEVP
2nd rowNEVP
3rd rowNEVP
4th rowNEVP
5th rowNEVP
ValueCountFrequency (%)
unspecified 5957
31.8%
unit 3838
20.5%
gps 3838
20.5%
geolocate 1254
 
6.7%
google 785
 
4.2%
earth 713
 
3.8%
vertnet 649
 
3.5%
2014 290
 
1.5%
census 290
 
1.5%
tiger 290
 
1.5%
Other values (11) 847
 
4.5%
2025-02-28T12:47:53.896949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 16291
12.4%
e 15708
12.0%
n 10145
 
7.7%
u 10138
 
7.7%
c 7413
 
5.7%
t 7162
 
5.5%
s 6614
 
5.0%
p 6243
 
4.8%
G 6167
 
4.7%
d 6099
 
4.6%
Other values (32) 39183
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 16291
12.4%
e 15708
12.0%
n 10145
 
7.7%
u 10138
 
7.7%
c 7413
 
5.7%
t 7162
 
5.5%
s 6614
 
5.0%
p 6243
 
4.8%
G 6167
 
4.7%
d 6099
 
4.6%
Other values (32) 39183
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 16291
12.4%
e 15708
12.0%
n 10145
 
7.7%
u 10138
 
7.7%
c 7413
 
5.7%
t 7162
 
5.5%
s 6614
 
5.0%
p 6243
 
4.8%
G 6167
 
4.7%
d 6099
 
4.6%
Other values (32) 39183
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 16291
12.4%
e 15708
12.0%
n 10145
 
7.7%
u 10138
 
7.7%
c 7413
 
5.7%
t 7162
 
5.5%
s 6614
 
5.0%
p 6243
 
4.8%
G 6167
 
4.7%
d 6099
 
4.6%
Other values (32) 39183
29.9%

georeferenceRemarks
Text

Missing 

Distinct562
Distinct (%)4.3%
Missing5661
Missing (%)30.0%
Memory size147.5 KiB
2025-02-28T12:47:54.024121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length570
Median length446
Mean length102.1251799
Min length8

Characters and Unicode

Total characters1348563
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

Unique291 ?
Unique (%)2.2%

Sample

1st rowprovisional georeference to ST CO PL: Connecticut Middlesex Clinton, 6 Mar 2015, LFG
2nd rowprovisional georeference to ST CO PL: Connecticut Middlesex Clinton, 6 Mar 2015, LFG
3rd rowprovisional georeference to ST CO PL: Connecticut Middlesex Clinton, 6 Mar 2015, LFG
4th rowprovisional georeference to ST CO PL: Connecticut Middlesex Clinton, 6 Mar 2015, LFG
5th rowprovisional georeference to ST CO PL: Connecticut Middlesex Clinton, 6 Mar 2015, LFG
ValueCountFrequency (%)
for 11797
 
5.4%
km 11604
 
5.3%
radius 10782
 
5.0%
georeference 7659
 
3.5%
to 6875
 
3.2%
by 5881
 
2.7%
was 5876
 
2.7%
that 5847
 
2.7%
only 5832
 
2.7%
ex 5813
 
2.7%
Other values (1631) 139388
64.1%
2025-02-28T12:47:54.230269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
204184
15.1%
e 140668
 
10.4%
r 102754
 
7.6%
i 71247
 
5.3%
o 67231
 
5.0%
s 56371
 
4.2%
a 55673
 
4.1%
n 55507
 
4.1%
t 49761
 
3.7%
d 47386
 
3.5%
Other values (74) 497781
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1348563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
204184
15.1%
e 140668
 
10.4%
r 102754
 
7.6%
i 71247
 
5.3%
o 67231
 
5.0%
s 56371
 
4.2%
a 55673
 
4.1%
n 55507
 
4.1%
t 49761
 
3.7%
d 47386
 
3.5%
Other values (74) 497781
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1348563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
204184
15.1%
e 140668
 
10.4%
r 102754
 
7.6%
i 71247
 
5.3%
o 67231
 
5.0%
s 56371
 
4.2%
a 55673
 
4.1%
n 55507
 
4.1%
t 49761
 
3.7%
d 47386
 
3.5%
Other values (74) 497781
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1348563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
204184
15.1%
e 140668
 
10.4%
r 102754
 
7.6%
i 71247
 
5.3%
o 67231
 
5.0%
s 56371
 
4.2%
a 55673
 
4.1%
n 55507
 
4.1%
t 49761
 
3.7%
d 47386
 
3.5%
Other values (74) 497781
36.9%

typeStatus
Text

Missing 

Distinct5
Distinct (%)22.7%
Missing18844
Missing (%)99.9%
Memory size147.5 KiB
2025-02-28T12:47:54.270654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.090909091
Min length8

Characters and Unicode

Total characters178
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 (%)9.1%

Sample

1st rowhypotype
2nd rowparatype
3rd rowhypotype
4th rowhypotype
5th rowhypotype
ValueCountFrequency (%)
hypotype 13
59.1%
paratype 5
 
22.7%
topotype 2
 
9.1%
plesiotype 1
 
4.5%
holotype 1
 
4.5%
2025-02-28T12:47:54.359848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 43
24.2%
y 35
19.7%
t 24
13.5%
e 23
12.9%
o 20
11.2%
h 14
 
7.9%
a 10
 
5.6%
r 5
 
2.8%
l 2
 
1.1%
s 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 43
24.2%
y 35
19.7%
t 24
13.5%
e 23
12.9%
o 20
11.2%
h 14
 
7.9%
a 10
 
5.6%
r 5
 
2.8%
l 2
 
1.1%
s 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 43
24.2%
y 35
19.7%
t 24
13.5%
e 23
12.9%
o 20
11.2%
h 14
 
7.9%
a 10
 
5.6%
r 5
 
2.8%
l 2
 
1.1%
s 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 43
24.2%
y 35
19.7%
t 24
13.5%
e 23
12.9%
o 20
11.2%
h 14
 
7.9%
a 10
 
5.6%
r 5
 
2.8%
l 2
 
1.1%
s 1
 
0.6%

identifiedBy
Text

Missing 

Distinct46
Distinct (%)4.1%
Missing17735
Missing (%)94.0%
Memory size147.5 KiB
2025-02-28T12:47:54.424051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length21
Mean length15.7020336
Min length6

Characters and Unicode

Total characters17759
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.9%

Sample

1st rowGary P. Aronsen
2nd rowGary P. Aronsen
3rd rowJosé A. Ottenwalder
4th rowAngus J. Mossman
5th rowAngus J. Mossman
ValueCountFrequency (%)
jordan 278
 
8.9%
colosi 278
 
8.9%
g 278
 
8.9%
a 247
 
7.9%
mary 240
 
7.7%
turner 240
 
7.7%
kristof 101
 
3.2%
zyskowski 101
 
3.2%
alex 100
 
3.2%
dornburg 100
 
3.2%
Other values (91) 1159
37.1%
2025-02-28T12:47:54.560606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1991
 
11.2%
r 1773
 
10.0%
o 1434
 
8.1%
n 1105
 
6.2%
a 1041
 
5.9%
e 976
 
5.5%
s 880
 
5.0%
i 864
 
4.9%
. 854
 
4.8%
l 730
 
4.1%
Other values (40) 6111
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1991
 
11.2%
r 1773
 
10.0%
o 1434
 
8.1%
n 1105
 
6.2%
a 1041
 
5.9%
e 976
 
5.5%
s 880
 
5.0%
i 864
 
4.9%
. 854
 
4.8%
l 730
 
4.1%
Other values (40) 6111
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1991
 
11.2%
r 1773
 
10.0%
o 1434
 
8.1%
n 1105
 
6.2%
a 1041
 
5.9%
e 976
 
5.5%
s 880
 
5.0%
i 864
 
4.9%
. 854
 
4.8%
l 730
 
4.1%
Other values (40) 6111
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1991
 
11.2%
r 1773
 
10.0%
o 1434
 
8.1%
n 1105
 
6.2%
a 1041
 
5.9%
e 976
 
5.5%
s 880
 
5.0%
i 864
 
4.9%
. 854
 
4.8%
l 730
 
4.1%
Other values (40) 6111
34.4%

dateIdentified
Text

Missing 

Distinct26
Distinct (%)2.7%
Missing17913
Missing (%)94.9%
Memory size147.5 KiB
2025-02-28T12:47:54.602425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3812
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.5%

Sample

1st row2016
2nd row2016
3rd row1985
4th row2016
5th row2016
ValueCountFrequency (%)
2008 271
28.4%
2009 257
27.0%
2007 130
13.6%
2012 126
13.2%
2016 26
 
2.7%
2011 22
 
2.3%
2020 22
 
2.3%
2010 22
 
2.3%
2024 18
 
1.9%
2023 15
 
1.6%
Other values (16) 44
 
4.6%
2025-02-28T12:47:54.686020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1656
43.4%
2 1137
29.8%
8 276
 
7.2%
9 274
 
7.2%
1 250
 
6.6%
7 132
 
3.5%
6 33
 
0.9%
4 25
 
0.7%
3 21
 
0.6%
5 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1656
43.4%
2 1137
29.8%
8 276
 
7.2%
9 274
 
7.2%
1 250
 
6.6%
7 132
 
3.5%
6 33
 
0.9%
4 25
 
0.7%
3 21
 
0.6%
5 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1656
43.4%
2 1137
29.8%
8 276
 
7.2%
9 274
 
7.2%
1 250
 
6.6%
7 132
 
3.5%
6 33
 
0.9%
4 25
 
0.7%
3 21
 
0.6%
5 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1656
43.4%
2 1137
29.8%
8 276
 
7.2%
9 274
 
7.2%
1 250
 
6.6%
7 132
 
3.5%
6 33
 
0.9%
4 25
 
0.7%
3 21
 
0.6%
5 8
 
0.2%

identificationRemarks
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing18863
Missing (%)> 99.9%
Memory size147.5 KiB
2025-02-28T12:47:54.721116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length57
Median length6
Mean length22.66666667
Min length5

Characters and Unicode

Total characters68
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

Unique3 ?
Unique (%)100.0%

Sample

1st rowreferenced on page 89 in the descripton of Agouti thomasi
2nd rowEaton
3rd rowThorpe
ValueCountFrequency (%)
referenced 1
8.3%
on 1
8.3%
page 1
8.3%
89 1
8.3%
in 1
8.3%
the 1
8.3%
descripton 1
8.3%
of 1
8.3%
agouti 1
8.3%
thomasi 1
8.3%
Other values (2) 2
16.7%
2025-02-28T12:47:54.804290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
13.2%
e 8
11.8%
o 7
10.3%
n 5
 
7.4%
t 5
 
7.4%
r 4
 
5.9%
i 4
 
5.9%
h 3
 
4.4%
a 3
 
4.4%
p 3
 
4.4%
Other values (12) 17
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9
13.2%
e 8
11.8%
o 7
10.3%
n 5
 
7.4%
t 5
 
7.4%
r 4
 
5.9%
i 4
 
5.9%
h 3
 
4.4%
a 3
 
4.4%
p 3
 
4.4%
Other values (12) 17
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9
13.2%
e 8
11.8%
o 7
10.3%
n 5
 
7.4%
t 5
 
7.4%
r 4
 
5.9%
i 4
 
5.9%
h 3
 
4.4%
a 3
 
4.4%
p 3
 
4.4%
Other values (12) 17
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9
13.2%
e 8
11.8%
o 7
10.3%
n 5
 
7.4%
t 5
 
7.4%
r 4
 
5.9%
i 4
 
5.9%
h 3
 
4.4%
a 3
 
4.4%
p 3
 
4.4%
Other values (12) 17
25.0%
Distinct2018
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:54.935800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length34
Mean length22.09201739
Min length3

Characters and Unicode

Total characters416788
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique703 ?
Unique (%)3.7%

Sample

1st rowTamias striatus fisheri
2nd rowPeromyscus leucopus noveboracensis
3rd rowPeromyscus leucopus noveboracensis
4th rowPeromyscus leucopus noveboracensis
5th rowPeromyscus leucopus noveboracensis
ValueCountFrequency (%)
peromyscus 1837
 
4.0%
cinereus 1489
 
3.2%
sorex 1193
 
2.6%
brevicauda 1125
 
2.4%
blarina 976
 
2.1%
zibethicus 898
 
2.0%
talpoides 868
 
1.9%
gapperi 848
 
1.8%
maniculatus 829
 
1.8%
leucopus 782
 
1.7%
Other values (2070) 35113
76.4%
2025-02-28T12:47:55.158938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 41623
 
10.0%
i 36625
 
8.8%
a 35093
 
8.4%
u 30890
 
7.4%
e 30381
 
7.3%
27092
 
6.5%
r 26522
 
6.4%
o 25267
 
6.1%
n 22452
 
5.4%
c 20781
 
5.0%
Other values (43) 120062
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 416788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 41623
 
10.0%
i 36625
 
8.8%
a 35093
 
8.4%
u 30890
 
7.4%
e 30381
 
7.3%
27092
 
6.5%
r 26522
 
6.4%
o 25267
 
6.1%
n 22452
 
5.4%
c 20781
 
5.0%
Other values (43) 120062
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 416788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 41623
 
10.0%
i 36625
 
8.8%
a 35093
 
8.4%
u 30890
 
7.4%
e 30381
 
7.3%
27092
 
6.5%
r 26522
 
6.4%
o 25267
 
6.1%
n 22452
 
5.4%
c 20781
 
5.0%
Other values (43) 120062
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 416788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 41623
 
10.0%
i 36625
 
8.8%
a 35093
 
8.4%
u 30890
 
7.4%
e 30381
 
7.3%
27092
 
6.5%
r 26522
 
6.4%
o 25267
 
6.1%
n 22452
 
5.4%
c 20781
 
5.0%
Other values (43) 120062
28.8%
Distinct256
Distinct (%)1.4%
Missing153
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:55.287269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length231
Median length222
Mean length176.5778336
Min length30

Characters and Unicode

Total characters3304301
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st rowAnimalia; Chordata; Vertebrata; Amniota; Mammalia; Theriiformes-----Theria-Placentalia-Epitheria; Preptotheria-Anagalida-Simplicidentata; Rodentia; Sciuromorpha; Sciurida; Sciuridae; Xerinae
2nd rowAnimalia; Chordata; Vertebrata; Amniota; Mammalia; Theriiformes-----Theria-Placentalia-Epitheria; Preptotheria-Anagalida-Simplicidentata; Rodentia; Myomorpha; Myodonta; Muroidea; Cricetidae; Neotominae
3rd rowAnimalia; Chordata; Vertebrata; Amniota; Mammalia; Theriiformes-----Theria-Placentalia-Epitheria; Preptotheria-Anagalida-Simplicidentata; Rodentia; Myomorpha; Myodonta; Muroidea; Cricetidae; Neotominae
4th rowAnimalia; Chordata; Vertebrata; Amniota; Mammalia; Theriiformes-----Theria-Placentalia-Epitheria; Preptotheria-Anagalida-Simplicidentata; Rodentia; Myomorpha; Myodonta; Muroidea; Cricetidae; Neotominae
5th rowAnimalia; Chordata; Vertebrata; Amniota; Mammalia; Theriiformes-----Theria-Placentalia-Epitheria; Preptotheria-Anagalida-Simplicidentata; Rodentia; Myomorpha; Myodonta; Muroidea; Cricetidae; Neotominae
ValueCountFrequency (%)
animalia 18713
 
8.8%
vertebrata 18713
 
8.8%
chordata 18713
 
8.8%
amniota 18711
 
8.8%
mammalia 18711
 
8.8%
theriiformes-----theria-placentalia-epitheria 15223
 
7.1%
rodentia 8426
 
3.9%
preptotheria-anagalida-simplicidentata 8425
 
3.9%
myomorpha 5919
 
2.8%
myodonta 5717
 
2.7%
Other values (374) 76277
35.7%
2025-02-28T12:47:55.485747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 452709
13.7%
i 335054
 
10.1%
e 250563
 
7.6%
r 228161
 
6.9%
t 207108
 
6.3%
; 194835
 
5.9%
194835
 
5.9%
o 167342
 
5.1%
- 154910
 
4.7%
n 124331
 
3.8%
Other values (40) 994453
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3304301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 452709
13.7%
i 335054
 
10.1%
e 250563
 
7.6%
r 228161
 
6.9%
t 207108
 
6.3%
; 194835
 
5.9%
194835
 
5.9%
o 167342
 
5.1%
- 154910
 
4.7%
n 124331
 
3.8%
Other values (40) 994453
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3304301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 452709
13.7%
i 335054
 
10.1%
e 250563
 
7.6%
r 228161
 
6.9%
t 207108
 
6.3%
; 194835
 
5.9%
194835
 
5.9%
o 167342
 
5.1%
- 154910
 
4.7%
n 124331
 
3.8%
Other values (40) 994453
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3304301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 452709
13.7%
i 335054
 
10.1%
e 250563
 
7.6%
r 228161
 
6.9%
t 207108
 
6.3%
; 194835
 
5.9%
194835
 
5.9%
o 167342
 
5.1%
- 154910
 
4.7%
n 124331
 
3.8%
Other values (40) 994453
30.1%

kingdom
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing153
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:55.527852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters149704
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 18713
100.0%
2025-02-28T12:47:55.606616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 37426
25.0%
a 37426
25.0%
A 18713
12.5%
n 18713
12.5%
m 18713
12.5%
l 18713
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 37426
25.0%
a 37426
25.0%
A 18713
12.5%
n 18713
12.5%
m 18713
12.5%
l 18713
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 37426
25.0%
a 37426
25.0%
A 18713
12.5%
n 18713
12.5%
m 18713
12.5%
l 18713
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 37426
25.0%
a 37426
25.0%
A 18713
12.5%
n 18713
12.5%
m 18713
12.5%
l 18713
12.5%

phylum
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing153
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:55.635498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters149704
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChordata
2nd rowChordata
3rd rowChordata
4th rowChordata
5th rowChordata
ValueCountFrequency (%)
chordata 18713
100.0%
2025-02-28T12:47:55.716991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 37426
25.0%
C 18713
12.5%
h 18713
12.5%
o 18713
12.5%
r 18713
12.5%
d 18713
12.5%
t 18713
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 37426
25.0%
C 18713
12.5%
h 18713
12.5%
o 18713
12.5%
r 18713
12.5%
d 18713
12.5%
t 18713
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 37426
25.0%
C 18713
12.5%
h 18713
12.5%
o 18713
12.5%
r 18713
12.5%
d 18713
12.5%
t 18713
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 37426
25.0%
C 18713
12.5%
h 18713
12.5%
o 18713
12.5%
r 18713
12.5%
d 18713
12.5%
t 18713
12.5%

class
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing155
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:55.749633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters149688
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMammalia
2nd rowMammalia
3rd rowMammalia
4th rowMammalia
5th rowMammalia
ValueCountFrequency (%)
mammalia 18711
100.0%
2025-02-28T12:47:55.830167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 56133
37.5%
m 37422
25.0%
M 18711
 
12.5%
l 18711
 
12.5%
i 18711
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 56133
37.5%
m 37422
25.0%
M 18711
 
12.5%
l 18711
 
12.5%
i 18711
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 56133
37.5%
m 37422
25.0%
M 18711
 
12.5%
l 18711
 
12.5%
i 18711
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 56133
37.5%
m 37422
25.0%
M 18711
 
12.5%
l 18711
 
12.5%
i 18711
 
12.5%

order
Text

Missing 

Distinct29
Distinct (%)0.2%
Missing401
Missing (%)2.1%
Memory size147.5 KiB
2025-02-28T12:47:55.860898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length8
Mean length9.418846466
Min length4

Characters and Unicode

Total characters173919
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRodentia
2nd rowRodentia
3rd rowRodentia
4th rowRodentia
5th rowRodentia
ValueCountFrequency (%)
rodentia 8426
45.6%
eulipotyphla 2517
 
13.6%
carnivora 2371
 
12.8%
artiodactyla 1530
 
8.3%
chiroptera 1102
 
6.0%
primates 953
 
5.2%
lagomorpha 348
 
1.9%
diprotodontia 248
 
1.3%
didelphimorphia 213
 
1.2%
perissodactyla 157
 
0.9%
Other values (19) 600
 
3.2%
2025-02-28T12:47:55.951687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 23290
13.4%
i 18724
10.8%
o 18258
10.5%
t 17054
9.8%
e 11398
 
6.6%
n 11252
 
6.5%
r 10808
 
6.2%
d 10797
 
6.2%
R 8426
 
4.8%
l 7208
 
4.1%
Other values (22) 36704
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 23290
13.4%
i 18724
10.8%
o 18258
10.5%
t 17054
9.8%
e 11398
 
6.6%
n 11252
 
6.5%
r 10808
 
6.2%
d 10797
 
6.2%
R 8426
 
4.8%
l 7208
 
4.1%
Other values (22) 36704
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 23290
13.4%
i 18724
10.8%
o 18258
10.5%
t 17054
9.8%
e 11398
 
6.6%
n 11252
 
6.5%
r 10808
 
6.2%
d 10797
 
6.2%
R 8426
 
4.8%
l 7208
 
4.1%
Other values (22) 36704
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 23290
13.4%
i 18724
10.8%
o 18258
10.5%
t 17054
9.8%
e 11398
 
6.6%
n 11252
 
6.5%
r 10808
 
6.2%
d 10797
 
6.2%
R 8426
 
4.8%
l 7208
 
4.1%
Other values (22) 36704
21.1%

family
Text

Missing 

Distinct130
Distinct (%)0.7%
Missing838
Missing (%)4.4%
Memory size147.5 KiB
2025-02-28T12:47:55.989256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.660749945
Min length6

Characters and Unicode

Total characters174164
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowSciuridae
2nd rowCricetidae
3rd rowCricetidae
4th rowCricetidae
5th rowCricetidae
ValueCountFrequency (%)
cricetidae 4134
22.9%
soricidae 2286
12.7%
sciuridae 1673
 
9.3%
muridae 1073
 
6.0%
bovidae 840
 
4.7%
canidae 662
 
3.7%
mustelidae 501
 
2.8%
dipodidae 458
 
2.5%
cercopithecidae 421
 
2.3%
vespertilionidae 407
 
2.3%
Other values (120) 5573
30.9%
2025-02-28T12:47:56.084112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 29146
16.7%
e 27525
15.8%
a 20548
11.8%
d 19366
11.1%
r 13299
7.6%
c 10222
 
5.9%
o 8596
 
4.9%
t 7181
 
4.1%
C 5980
 
3.4%
S 4069
 
2.3%
Other values (34) 28232
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 174164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 29146
16.7%
e 27525
15.8%
a 20548
11.8%
d 19366
11.1%
r 13299
7.6%
c 10222
 
5.9%
o 8596
 
4.9%
t 7181
 
4.1%
C 5980
 
3.4%
S 4069
 
2.3%
Other values (34) 28232
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 174164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 29146
16.7%
e 27525
15.8%
a 20548
11.8%
d 19366
11.1%
r 13299
7.6%
c 10222
 
5.9%
o 8596
 
4.9%
t 7181
 
4.1%
C 5980
 
3.4%
S 4069
 
2.3%
Other values (34) 28232
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 174164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 29146
16.7%
e 27525
15.8%
a 20548
11.8%
d 19366
11.1%
r 13299
7.6%
c 10222
 
5.9%
o 8596
 
4.9%
t 7181
 
4.1%
C 5980
 
3.4%
S 4069
 
2.3%
Other values (34) 28232
16.2%

genus
Text

Missing 

Distinct610
Distinct (%)3.5%
Missing1196
Missing (%)6.3%
Memory size147.5 KiB
2025-02-28T12:47:56.218203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.099717035
Min length3

Characters and Unicode

Total characters143122
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)0.6%

Sample

1st rowTamias
2nd rowPeromyscus
3rd rowPeromyscus
4th rowPeromyscus
5th rowPeromyscus
ValueCountFrequency (%)
peromyscus 1837
 
10.4%
sorex 1193
 
6.8%
blarina 976
 
5.5%
clethrionomys 742
 
4.2%
ondatra 631
 
3.6%
microtus 435
 
2.5%
tamias 398
 
2.3%
napaeozapus 365
 
2.1%
canis 345
 
2.0%
procyon 329
 
1.9%
Other values (600) 10419
59.0%
2025-02-28T12:47:56.498830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 15060
 
10.5%
o 13424
 
9.4%
a 11837
 
8.3%
r 11272
 
7.9%
e 9347
 
6.5%
u 9121
 
6.4%
i 8228
 
5.7%
c 6244
 
4.4%
y 5923
 
4.1%
l 5700
 
4.0%
Other values (37) 46966
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 15060
 
10.5%
o 13424
 
9.4%
a 11837
 
8.3%
r 11272
 
7.9%
e 9347
 
6.5%
u 9121
 
6.4%
i 8228
 
5.7%
c 6244
 
4.4%
y 5923
 
4.1%
l 5700
 
4.0%
Other values (37) 46966
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 15060
 
10.5%
o 13424
 
9.4%
a 11837
 
8.3%
r 11272
 
7.9%
e 9347
 
6.5%
u 9121
 
6.4%
i 8228
 
5.7%
c 6244
 
4.4%
y 5923
 
4.1%
l 5700
 
4.0%
Other values (37) 46966
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 15060
 
10.5%
o 13424
 
9.4%
a 11837
 
8.3%
r 11272
 
7.9%
e 9347
 
6.5%
u 9121
 
6.4%
i 8228
 
5.7%
c 6244
 
4.4%
y 5923
 
4.1%
l 5700
 
4.0%
Other values (37) 46966
32.8%

specificEpithet
Text

Missing 

Distinct954
Distinct (%)5.8%
Missing2296
Missing (%)12.2%
Memory size147.5 KiB
2025-02-28T12:47:56.639893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length16
Mean length8.552082076
Min length2

Characters and Unicode

Total characters141708
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

Unique231 ?
Unique (%)1.4%

Sample

1st rowstriatus
2nd rowleucopus
3rd rowleucopus
4th rowleucopus
5th rowleucopus
ValueCountFrequency (%)
brevicauda 987
 
6.0%
leucopus 775
 
4.7%
cinereus 746
 
4.5%
gapperi 708
 
4.3%
maniculatus 683
 
4.1%
zibethicus 631
 
3.8%
insignis 365
 
2.2%
lotor 328
 
2.0%
canadensis 320
 
1.9%
hudsonicus 292
 
1.8%
Other values (942) 10743
64.8%
2025-02-28T12:47:56.846498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 16262
11.5%
s 15024
10.6%
u 14751
10.4%
a 13768
9.7%
e 10570
 
7.5%
n 9249
 
6.5%
r 9097
 
6.4%
c 8731
 
6.2%
l 6248
 
4.4%
t 5924
 
4.2%
Other values (17) 32084
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 16262
11.5%
s 15024
10.6%
u 14751
10.4%
a 13768
9.7%
e 10570
 
7.5%
n 9249
 
6.5%
r 9097
 
6.4%
c 8731
 
6.2%
l 6248
 
4.4%
t 5924
 
4.2%
Other values (17) 32084
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 16262
11.5%
s 15024
10.6%
u 14751
10.4%
a 13768
9.7%
e 10570
 
7.5%
n 9249
 
6.5%
r 9097
 
6.4%
c 8731
 
6.2%
l 6248
 
4.4%
t 5924
 
4.2%
Other values (17) 32084
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 16262
11.5%
s 15024
10.6%
u 14751
10.4%
a 13768
9.7%
e 10570
 
7.5%
n 9249
 
6.5%
r 9097
 
6.4%
c 8731
 
6.2%
l 6248
 
4.4%
t 5924
 
4.2%
Other values (17) 32084
22.6%

infraspecificEpithet
Text

Missing 

Distinct755
Distinct (%)7.3%
Missing8470
Missing (%)44.9%
Memory size147.5 KiB
2025-02-28T12:47:56.994208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length14
Mean length9.011735283
Min length3

Characters and Unicode

Total characters93686
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

Unique255 ?
Unique (%)2.5%

Sample

1st rowfisheri
2nd rownoveboracensis
3rd rownoveboracensis
4th rownoveboracensis
5th rownoveboracensis
ValueCountFrequency (%)
talpoides 835
 
8.0%
cinereus 743
 
7.1%
noveboracensis 678
 
6.5%
insignis 368
 
3.5%
ochraceus 358
 
3.4%
pennsylvanicus 303
 
2.9%
fumeus 275
 
2.6%
zibethicus 267
 
2.6%
gracilis 226
 
2.2%
domesticus 193
 
1.9%
Other values (745) 6154
59.2%
2025-02-28T12:47:57.195944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 11487
12.3%
i 10927
11.7%
e 8921
9.5%
a 7771
8.3%
n 7368
 
7.9%
u 6877
 
7.3%
c 5749
 
6.1%
r 5693
 
6.1%
o 5382
 
5.7%
l 4160
 
4.4%
Other values (17) 19351
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 11487
12.3%
i 10927
11.7%
e 8921
9.5%
a 7771
8.3%
n 7368
 
7.9%
u 6877
 
7.3%
c 5749
 
6.1%
r 5693
 
6.1%
o 5382
 
5.7%
l 4160
 
4.4%
Other values (17) 19351
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 11487
12.3%
i 10927
11.7%
e 8921
9.5%
a 7771
8.3%
n 7368
 
7.9%
u 6877
 
7.3%
c 5749
 
6.1%
r 5693
 
6.1%
o 5382
 
5.7%
l 4160
 
4.4%
Other values (17) 19351
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 11487
12.3%
i 10927
11.7%
e 8921
9.5%
a 7771
8.3%
n 7368
 
7.9%
u 6877
 
7.3%
c 5749
 
6.1%
r 5693
 
6.1%
o 5382
 
5.7%
l 4160
 
4.4%
Other values (17) 19351
20.7%
Distinct12
Distinct (%)0.1%
Missing153
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:57.238441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.513119222
Min length5

Characters and Unicode

Total characters159306
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSubspecies
2nd rowSubspecies
3rd rowSubspecies
4th rowSubspecies
5th rowSubspecies
ValueCountFrequency (%)
subspecies 10397
55.6%
species 6125
32.7%
genus 1098
 
5.9%
family 507
 
2.7%
class 246
 
1.3%
order 142
 
0.8%
superfamily 117
 
0.6%
subfamily 49
 
0.3%
suborder 26
 
0.1%
infraorder 3
 
< 0.1%
Other values (2) 3
 
< 0.1%
2025-02-28T12:47:57.323568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34431
21.6%
s 28509
17.9%
i 17196
10.8%
S 16716
10.5%
p 16641
10.4%
c 16522
10.4%
u 11691
 
7.3%
b 10475
 
6.6%
n 1101
 
0.7%
G 1098
 
0.7%
Other values (14) 4926
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34431
21.6%
s 28509
17.9%
i 17196
10.8%
S 16716
10.5%
p 16641
10.4%
c 16522
10.4%
u 11691
 
7.3%
b 10475
 
6.6%
n 1101
 
0.7%
G 1098
 
0.7%
Other values (14) 4926
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34431
21.6%
s 28509
17.9%
i 17196
10.8%
S 16716
10.5%
p 16641
10.4%
c 16522
10.4%
u 11691
 
7.3%
b 10475
 
6.6%
n 1101
 
0.7%
G 1098
 
0.7%
Other values (14) 4926
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34431
21.6%
s 28509
17.9%
i 17196
10.8%
S 16716
10.5%
p 16641
10.4%
c 16522
10.4%
u 11691
 
7.3%
b 10475
 
6.6%
n 1101
 
0.7%
G 1098
 
0.7%
Other values (14) 4926
 
3.1%
Distinct1069
Distinct (%)5.8%
Missing385
Missing (%)2.0%
Memory size147.5 KiB
2025-02-28T12:47:57.449723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length93
Median length52
Mean length14.2923002
Min length4

Characters and Unicode

Total characters264136
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)1.7%

Sample

1st rowHowell, 1925
2nd row(Fischer, 1829)
3rd row(Fischer, 1829)
4th row(Fischer, 1829)
5th row(Fischer, 1829)
ValueCountFrequency (%)
linnaeus 2832
 
7.1%
1758 2192
 
5.5%
miller 1126
 
2.8%
1830 1009
 
2.5%
1792 1008
 
2.5%
kerr 992
 
2.5%
gapper 835
 
2.1%
1766 748
 
1.9%
fischer 739
 
1.8%
1829 716
 
1.8%
Other values (587) 27903
69.6%
2025-02-28T12:47:57.645054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22049
 
8.3%
21619
 
8.2%
, 18697
 
7.1%
e 16160
 
6.1%
8 14713
 
5.6%
r 12026
 
4.6%
n 11112
 
4.2%
a 10637
 
4.0%
( 10300
 
3.9%
) 10300
 
3.9%
Other values (64) 116523
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22049
 
8.3%
21619
 
8.2%
, 18697
 
7.1%
e 16160
 
6.1%
8 14713
 
5.6%
r 12026
 
4.6%
n 11112
 
4.2%
a 10637
 
4.0%
( 10300
 
3.9%
) 10300
 
3.9%
Other values (64) 116523
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22049
 
8.3%
21619
 
8.2%
, 18697
 
7.1%
e 16160
 
6.1%
8 14713
 
5.6%
r 12026
 
4.6%
n 11112
 
4.2%
a 10637
 
4.0%
( 10300
 
3.9%
) 10300
 
3.9%
Other values (64) 116523
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22049
 
8.3%
21619
 
8.2%
, 18697
 
7.1%
e 16160
 
6.1%
8 14713
 
5.6%
r 12026
 
4.6%
n 11112
 
4.2%
a 10637
 
4.0%
( 10300
 
3.9%
) 10300
 
3.9%
Other values (64) 116523
44.1%
Distinct1166
Distinct (%)6.2%
Missing153
Missing (%)0.8%
Memory size147.5 KiB
2025-02-28T12:47:57.784514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length143
Median length121
Mean length82.7007428
Min length31

Characters and Unicode

Total characters1547579
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

Unique294 ?
Unique (%)1.6%

Sample

1st rowEastern Chipmunk; chipmunks; squirrels; rodents; mammals; vertebrates; chordates; animals
2nd rowWhite-footed Mouse; mice; rodents; mammals; vertebrates; chordates; animals
3rd rowWhite-footed Mouse; mice; rodents; mammals; vertebrates; chordates; animals
4th rowWhite-footed Mouse; mice; rodents; mammals; vertebrates; chordates; animals
5th rowWhite-footed Mouse; mice; rodents; mammals; vertebrates; chordates; animals
ValueCountFrequency (%)
mammals 18748
 
11.1%
vertebrates 18713
 
11.1%
chordates 18713
 
11.1%
animals 18713
 
11.1%
rodents 8561
 
5.1%
mice 7296
 
4.3%
carnivores 4733
 
2.8%
shrews 3336
 
2.0%
mouse 2787
 
1.7%
squirrels 2585
 
1.5%
Other values (1028) 64018
38.1%
2025-02-28T12:47:57.995776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 152163
 
9.8%
e 149527
 
9.7%
149490
 
9.7%
s 133715
 
8.6%
; 118068
 
7.6%
r 116349
 
7.5%
t 95576
 
6.2%
m 94542
 
6.1%
o 69125
 
4.5%
l 60718
 
3.9%
Other values (50) 408306
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1547579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 152163
 
9.8%
e 149527
 
9.7%
149490
 
9.7%
s 133715
 
8.6%
; 118068
 
7.6%
r 116349
 
7.5%
t 95576
 
6.2%
m 94542
 
6.1%
o 69125
 
4.5%
l 60718
 
3.9%
Other values (50) 408306
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1547579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 152163
 
9.8%
e 149527
 
9.7%
149490
 
9.7%
s 133715
 
8.6%
; 118068
 
7.6%
r 116349
 
7.5%
t 95576
 
6.2%
m 94542
 
6.1%
o 69125
 
4.5%
l 60718
 
3.9%
Other values (50) 408306
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1547579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 152163
 
9.8%
e 149527
 
9.7%
149490
 
9.7%
s 133715
 
8.6%
; 118068
 
7.6%
r 116349
 
7.5%
t 95576
 
6.2%
m 94542
 
6.1%
o 69125
 
4.5%
l 60718
 
3.9%
Other values (50) 408306
26.4%

nomenclaturalCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:58.036664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters75464
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 rowICZN
2nd rowICZN
3rd rowICZN
4th rowICZN
5th rowICZN
ValueCountFrequency (%)
iczn 18866
100.0%
2025-02-28T12:47:58.116810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 18866
25.0%
C 18866
25.0%
Z 18866
25.0%
N 18866
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 18866
25.0%
C 18866
25.0%
Z 18866
25.0%
N 18866
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 18866
25.0%
C 18866
25.0%
Z 18866
25.0%
N 18866
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 18866
25.0%
C 18866
25.0%
Z 18866
25.0%
N 18866
25.0%

taxonRemarks
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 KiB
2025-02-28T12:47:58.147852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length41
Median length41
Mean length41
Min length41

Characters and Unicode

Total characters773506
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimals and Plants: Vertebrates - Mammals
2nd rowAnimals and Plants: Vertebrates - Mammals
3rd rowAnimals and Plants: Vertebrates - Mammals
4th rowAnimals and Plants: Vertebrates - Mammals
5th rowAnimals and Plants: Vertebrates - Mammals
ValueCountFrequency (%)
animals 18866
16.7%
and 18866
16.7%
plants 18866
16.7%
vertebrates 18866
16.7%
18866
16.7%
mammals 18866
16.7%
2025-02-28T12:47:58.240780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 113196
14.6%
94330
12.2%
s 75464
9.8%
e 56598
 
7.3%
m 56598
 
7.3%
l 56598
 
7.3%
n 56598
 
7.3%
t 56598
 
7.3%
r 37732
 
4.9%
A 18866
 
2.4%
Other values (8) 150928
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 773506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 113196
14.6%
94330
12.2%
s 75464
9.8%
e 56598
 
7.3%
m 56598
 
7.3%
l 56598
 
7.3%
n 56598
 
7.3%
t 56598
 
7.3%
r 37732
 
4.9%
A 18866
 
2.4%
Other values (8) 150928
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 773506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 113196
14.6%
94330
12.2%
s 75464
9.8%
e 56598
 
7.3%
m 56598
 
7.3%
l 56598
 
7.3%
n 56598
 
7.3%
t 56598
 
7.3%
r 37732
 
4.9%
A 18866
 
2.4%
Other values (8) 150928
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 773506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 113196
14.6%
94330
12.2%
s 75464
9.8%
e 56598
 
7.3%
m 56598
 
7.3%
l 56598
 
7.3%
n 56598
 
7.3%
t 56598
 
7.3%
r 37732
 
4.9%
A 18866
 
2.4%
Other values (8) 150928
19.5%