Skip to main content

PyOpenFisheries

PyOpenFisheries makes it even easier to access the OpenFisheries API in Python.

A good usecase of this library is to gather data to plot in a Jupyter Notebook, or to collect data to run time-series analysis on.

Learn more about OpenFisheries.org.

PyOpenFisheries(**kwargs)

Bases: object

Base class for accessing the OpenFisheries API. Useful for gathering data for plots or analysis.

Returns:

instance: base OpenFisheries API wrapper

Examples:

>>> open_fish_conn = PyOpenFisheries()
>>> skipjack_tuna = open_fish_conn.annual_landings(species="SKJ").filter_years(start_year=1970,end_year=1991)
>>> print(skipjack_tuna.landings)
[{'year': 1970, 'catch': 402166}...{'year': 1991, 'catch': 1575170}]
>>> print(skipjack_tuna.summarize())
Landings of SKJ globally from 1970 to 1991

Attributes:

landings: List of dictionaries containing the year and landing count.
species: if present - three-letter ASFIS species code (i.e. “SKJ” - Skipjack Tuna).
country: if present - ISO-3166 alpha 3 country code (i.e. “USA” - United States).
start_year: if present - start year of filtered landings data.
end_year : if present - end year of filtered landings data.

annual_landings(species=None, country=None)

Gathers annual fishery landings filtered by either species or country. If neither fish nor country are specified, then this will return global aggregate landings data.

Args:

species: three-letter ASFIS species code (i.e. “SKJ” - Skipjack Tuna)
country: ISO-3166 alpha 3 country code (i.e. “USA” - United States)

Returns:

instance: PyOpenFisheries instance with landings populated

filter_years(start_year=1950, end_year=2018)

Filters annual fishing data to within a time-frame.

Args:

start_year: 4 digit integer year (i.e. 1980)
end_year: 4 digit integer year (i.e. 2015)

Returns:

instance: PyOpenFisheries instance with years filtered.

summarize()

Summarizes what has been returned from OpenFisheries.

label()

Useful as a legend / for plots.

Metadata

Release files for pyopenfisheries 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyopenfisheries 0.1.2
File Size Uploaded Provenance Actions
PyOpenFisheries-0.1.2.tar.gz 7.4 kB Without provenance Details for PyOpenFisheries-0.1.2.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for pyopenfisheries 0.1.2
File Interpreter ABI Platform Actions
PyOpenFisheries-0.1.2-py3-none-any.whl Python 3 none any Details for PyOpenFisheries-0.1.2-py3-none-any.whl

Total release size: 19.4 kB

Release files / PyOpenFisheries-0.1.2.tar.gz

Download URL PyOpenFisheries-0.1.2.tar.gz
Size 7.4 kB
Tags Source
SHA-256 checksum
How to use checksums
ef45b3a7dc28dfea8de89728c8b4b4c1054e5ba9333d2d4891ee4972589cdd02
BLAKE2b-256 checksum
How to use checksums
1810b0018f6484ce89df1487deac08da83b59dc96e2552e41c001f70cc984e90
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.1.4 CPython/3.8.5 Linux/5.8.0-44-generic

Release files / PyOpenFisheries-0.1.2-py3-none-any.whl

Download URL PyOpenFisheries-0.1.2-py3-none-any.whl
Size 12.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b2b5b6776049ad2a10e003a7dcba8d91641ea07e5685e6ead943780ddeee5811
BLAKE2b-256 checksum
How to use checksums
fdde3a3ce794429ebad7321653cc47da561513aeae366437496c90e8a2b062de
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.1.4 CPython/3.8.5 Linux/5.8.0-44-generic

Security for pyopenfisheries 0.1.2

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page