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Ecology","sub_category":"Input Output Model","monthly_downloads":260,"total_dependent_repos":1,"total_dependent_packages":1,"readme":".. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n    :target: https://github.com/psf/black\n\n.. image:: https://readthedocs.org/projects/mario-suite/badge/?version=latest\n    :target: https://mario-suite.readthedocs.io/en/latest/index.html\n    :alt: Documentation Status\n\n.. image:: https://badge.fury.io/py/mariopy.svg\n    :target: https://badge.fury.io/py/mariopy\n\n.. image:: https://zenodo.org/badge/421900437.svg\n   :target: https://doi.org/10.5281/zenodo.5879382\n\n*******\nMARIO\n*******\n\n**MARIO** stands for **Multifunctional Analysis of Regions through Input-Output**.\nIt is a Python package for working with Input-Output Tables (IOT) and Supply and\nUse Tables (SUT). Once parsed, a table becomes a MARIO *database* that can be\ninspected, computed, transformed, aggregated, shocked, and exported.\n\nDocumentation is available on `Read the Docs \u003chttps://mario-suite.readthedocs.io/en/latest/index.html\u003e`_.\n\n\nWhat MARIO Supports\n-------------------\n\nMARIO is designed around a practical IO workflow:\n\n* parse a database from supported sources or load a packaged test table;\n* inspect sets, scenarios, and available matrices;\n* compute derived matrices and indicators on demand;\n* transform, aggregate, or shock the database;\n* export the results for roundtrip or downstream analysis.\n\nThe current documentation covers both standard parsers and custom database\ningestion. Supported workflows include:\n\n* single-region and multi-region systems;\n* monetary and hybrid tables where the parser supports them;\n* standard sources such as EXIOBASE, EORA, EUROSTAT, FIGARO, WIOD, OECD, and more;\n* custom databases from Excel, text, CSV, and pandas-based inputs;\n* aggregation, SUT-to-IOT conversion, scenario analysis, and exports.\n\n\nInstallation\n------------\n\nThe package name on PyPI is ``mariopy``, while the import name is ``mario``.\n\nPreferably, create a clean Python environment first:\n\n.. code-block:: bash\n\n   conda create -n mario python=3.11\n   conda activate mario\n\nInstall from PyPI:\n\n.. code-block:: bash\n\n   pip install mariopy\n\nParquet import and export support is included in the default installation.\n\nInstall from source:\n\n.. code-block:: bash\n\n   git clone https://github.com/it-is-me-mario/MARIO.git\n   cd MARIO\n   pip install -e .\n\n\nQuickstart\n----------\n\nA minimal test database is bundled with MARIO:\n\n.. code-block:: python\n\n   import mario\n\n   db = mario.load_test(\"IOT\")\n\n   print(db)\n   print(db.get_index(\"Region\"))\n\n   db.calc_all()\n   db.to_excel(path=\"output_folder\")\n\nFor SUT workflows:\n\n.. code-block:: python\n\n   import mario\n\n   sut = mario.load_test(\"SUT\")\n   iot = sut.to_iot(method=\"B\")\n\n\nDocumentation Map\n-----------------\n\nThe published documentation is organized into a few main sections:\n\n* `Setup \u003chttps://mario-suite.readthedocs.io/en/latest/setup/index.html\u003e`_ for installation and first checks;\n* `Concepts \u003chttps://mario-suite.readthedocs.io/en/latest/concepts/index.html\u003e`_ for MARIO terminology and conventions;\n* `User guide \u003chttps://mario-suite.readthedocs.io/en/latest/user_guide/index.html\u003e`_ for parsers, inspection, transformations, custom databases, and exports;\n* `API reference \u003chttps://mario-suite.readthedocs.io/en/latest/reference/api_library.html\u003e`_ for method-level documentation;\n* `Publications \u003chttps://mario-suite.readthedocs.io/en/latest/resources/publications.html\u003e`_ for the software paper and related research.\n\n\nCitation\n--------\n\nCitation guidance and the up-to-date list of publications using MARIO are maintained in the\n`Research section of the documentation \u003chttps://mario-suite.readthedocs.io/en/latest/resources/publications.html\u003e`_.\n\n\nLicense\n-------\n\nMARIO is distributed under the\n`GNU General Public License v3.0 \u003chttps://www.gnu.org/licenses/gpl-3.0.en.html\u003e`_.\n\n\nSupporting institutions\n-----------------------\n\nMARIO grows across two complementary settings.\n\n.. raw:: html\n\n    \u003ctable\u003e\n       \u003ctr\u003e\n          \u003ctd valign=\"top\" width=\"180\"\u003e\n             \u003ca href=\"https://www.polimi.it\"\u003e\n                \u003cimg src=\"https://www.polimi.it/_assets/4b51f00386267395f41e0940abbcd656/Images/logo.svg\" alt=\"Politecnico di Milano\" width=\"120\" /\u003e\n             \u003c/a\u003e\n          \u003c/td\u003e\n          \u003ctd valign=\"top\"\u003e\n             \u003cstrong\u003ePolitecnico di Milano\u003c/strong\u003e. 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As a spin-off of Politecnico di Milano, eNextGen brings MARIO into applied settings for companies and organisations, where the same analytical core supports real decarbonisation, sustainability, and decision-support cases.\n          \u003c/td\u003e\n       \u003c/tr\u003e\n    \u003c/table\u003e\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.5879382"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/191420","html_url":"https://ost.ecosyste.ms/projects/191420"}