Tutorial#
Note: You will need to register your email address at the atlas you want to download data for, otherwise you will get no data!
Now that you have successfully installed galah-python, we’ll provide a quick introduction on the functions you will
mainly be using to get data. If you’re looking for a quick reference guide for commands, the
User Guide collates all the available commands with examples.
This tutorial serves as an initial method to get you used to using different commands.
Configuring galah#
First, you will need to set some stored parameters to get full use out of the galah package. There are two
key parameters that you will need to set, especially to get occurrences: atlas and email.
Choosing an Atlas
First, you will need to choose an atlas to get information from. If you’re not sure what atlases galah-python
has on offer, run the command
>> import galah
>> galah.show_all(atlases=True)
and a list like this will appear:
atlas institution acronym url
0 Australia Atlas of Living Australia ALA https://www.ala.org.au
1 Austria Biodiversitäts-Atlas Österreich BAO https://biodiversityatlas.at
2 Brazil Sistemas de Informações sobre a Biodiversidade Brasileira SiBBr https://sibbr.gov.br
3 Flanders Vlaams Biodiversiteitsportaal VBP https://natuurdata.inbo.be
4 France Portail français d'accès aux données d'observation sur les espèces OpenObs https://inpn.mnhn.fr
5 Global Global Biodiversity Information Facility GBIF https://gbif.org
6 Guatemala Sistema Nacional de Información sobre Diversidad Biológica de Guatemala SNIBgt https://snib.conap.gob.gt
7 Kew Kew Data Portal KDP https://data.kew.org
8 Portugal GBIF Portugal GBIF.pt https://www.gbif.pt
9 Spain GBIF Spain GBIF.es https://www.gbif.es
10 Sweden Swedish Biodiversity Data Infrastructure SDBI https://biodiversitydata.se
11 United Kingdom National Biodiversity Network NBN https://nbn.org.uk
To choose an atlas, select the region that the atlas represents. By default, the atlas is set to Australia, which is
what we will sue for this example. However, for those interested in the other atlases on offer, say the Brazilian atlas,
type
>>> galah.galah_config(atlas="Brazil")
Storing Your Email
To download data from the atlases, you will need a registered email address. For the ALA, go to https://auth.ala.org.au/userdetails/registration/createAccount.
Once you have registered your email, you can store it in galah like so:
>>> import galah
>>> galah.galah_config(email="youremail@example.com")
This will not return anything. No error messages means it is configured correctly. To see what your configuation settings are, type
>>> galah.galah_config()
Configuration Value
0 email amanda.buyan@csiro.au
1 email_notify False
2 atlas Australia
3 data_profile ALA
4 ranks all
5 reason 10
6 verbose False
7 timeout 600
8 usernamegbif
9 passwordgbif
10 authenticate False
11 client_id
12 client_secret
13 access_token
14 refresh_token
15 scopes
16 expires_at
Building queries#
Now that galah is configured, we will get counts of records, so you know how many you are downloading. To see how
many records are currently in the ALA, type
>>> galah.atlas_counts()
totalRecords
0 180094883
If you are not interested in a specific species, but in the number of records in the atlas from the year 2020 onwards, you can
add this to the filters argument of atlas_counts().
>>> galah.atlas_counts(filters="year>=2020")
totalRecords
0 64291342
If you are wondering how the number of records for all species in the ALA changed over each year from 2020 onwards, you can
tell galah to group your results by year, to get yearly counts.
>>> galah.atlas_counts(filters="year>=2020",group_by="year")
year count
0 2020 8272730
1 2021 9643278
2 2022 10366308
3 2023 12061086
4 2024 12984832
5 2025 9908752
6 2026 1054356
To narrow down your search by a specific species, you can use the search_taxa() function to check whether or not the
taxonomic information for the species you are wanting to search. For this example, lLet’s choose the taxa Vulpes vulpes,
or the red fox.
>>> galah.search_taxa(taxa="Vulpes vulpes")
scientificName scientificNameAuthorship taxonConceptID rank matchType kingdom phylum classs order family genus species issues vernacularName
0 Vulpes vulpes Linnaeus, 1758 https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 species exactMatch Animalia Chordata Mammalia Carnivora Canidae Vulpes Vulpes vulpes noIssue Fox
Now that we can see we indeed have the red fox, we can see how many records the ALA has of the red fox.
>>> import galah
>>> galah.atlas_counts(taxa="Vulpes vulpes")
totalRecords
0 158999
Now, we can put our filters query together with our red fox query, to see how many occurrences of red foxes in the ALA
were seen each year from 2020 onwards.
>>> import galah
>>> galah.atlas_counts(taxa="Vulpes vulpes",filters="year>=2020",group_by="year")
year count
0 2020 6662
1 2021 8301
2 2022 8287
3 2023 8523
4 2024 8778
5 2025 8819
6 2026 1721
Downloading records#
Now that we know the number of red fox occurrences in each year starting with 2020, we will now download these records.
To do this, we will take the query from above and change the function name from atlas_counts() to atlas_occurrences().
>>> import galah
>>> galah.atlas_occurrences(taxa="Vulpes vulpes",filters="year>=2020")
decimalLatitude decimalLongitude eventDate scientificName taxonConceptID recordID dataResourceName occurrenceStatus
0 -39.136718 146.373940 2025-05-24T00:00:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 748efb94-997e-4ce8-948d-4eab3843fb19 FeralScan PRESENT
1 -39.083564 146.383184 2023-02-02T00:00:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 572341b6-94ca-4d96-bf4f-889cbd5e26f0 FeralScan PRESENT
2 -39.055892 146.448243 2023-09-26T12:58:13Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 c9e41540-1253-4db8-b13a-25b353dc93b5 iNaturalist Australia PRESENT
3 -39.048423 146.365914 2021-03-05T00:00:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 9869adbf-26c2-4258-a2c2-c76517f3b5ea FeralScan PRESENT
4 -39.043553 146.465719 2024-07-02T00:00:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 e4135950-eb4d-47aa-97fb-11ad8a75032a FeralScan PRESENT
... ... ... ... ... ... ... ... ...
51086 50.800000 -0.400000 2021-06-07T09:33:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 1e90a2e4-e0f5-4f2c-a7ce-97d3d465a1f4 Earth Guardians Weekly Feed PRESENT
51087 51.100000 -0.500000 2023-03-17T12:04:54Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 65fc1040-5675-4f6c-8c92-40f8de159bed Earth Guardians Weekly Feed PRESENT
51088 51.400000 -2.300000 2021-05-24T19:37:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 abc59803-000c-47bd-b758-8420dfce4714 Earth Guardians Weekly Feed PRESENT
51089 51.574697 -0.109975 2020-04-14T17:10:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 89d22e27-4c17-430d-90e3-74455cc78ec2 Earth Guardians Weekly Feed PRESENT
51090 51.900000 -0.200000 2025-07-14T08:53:00Z Vulpes vulpes https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 0bebb889-7a1c-44a6-ac42-33ab06023fa7 Earth Guardians Weekly Feed PRESENT
[51091 rows x 8 columns]
If you are only interested in the scientific name, as well as latitude and longitude, use the fields option as follows:
import galah
galah.atlas_occurrences(taxa="Vulpes vulpes",filters="year>=2020",fields=["scientificName","decimalLatitude","decimalLongitude"])
scientificName decimalLatitude decimalLongitude
0 Vulpes vulpes -33.417175 149.843869
1 Vulpes vulpes -27.972832 153.182574
2 Vulpes vulpes -37.189700 143.605000
3 Vulpes vulpes -36.679991 149.839487
4 Vulpes vulpes -28.172183 152.689295
... ... ... ...
51086 Vulpes vulpes -33.885489 150.609304
51087 Vulpes vulpes -38.465258 144.089698
51088 Vulpes vulpes -37.388872 144.267077
51089 Vulpes vulpes -30.545839 149.124897
51090 Vulpes vulpes -38.327331 142.637334
[51091 rows x 3 columns]
Check out other vignettes and the API docs for more information on how to use each of these functions, as well as to learn more about searching for information on how to filter your data.