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The dataset selector identifies different source families. National IOT and SUT workflows use different inputs and country/year options.

Import#

import mario

db = mario.parse_oecd(path="/path/to/ICIO2022_SML.csv", dataset="ICIO")

Result · A MARIO database. Inspect db, db.get_index("all") and db.units before selecting quantities for your analysis.

Inputs and options#

Relevant source pages

MARIO does not download OECD files automatically. For ICIO and national IOT, the expected workflow is to download the source files manually and then point the parser to the local files. For SUT, MARIO pulls the data directly from the official OECD SDMX API.

Key arguments

The key public arguments are:

  • path: one local OECD file or one directory containing multiple OECD files;

  • dataset: choose "ICIO", "IOT", or "SUT";

  • year: used to disambiguate directories and required for dataset="SUT";

  • country: used to disambiguate national IOT files and required for dataset="SUT".

Direct file path vs directory path

Use a direct file path when you already know the exact OECD file to parse. This is the simplest workflow and usually means you do not need any further disambiguation.

Use a directory path when you keep several OECD files together. In that case MARIO scans the directory and then uses dataset=, year=, and sometimes country= to pick the correct candidate.

Expected local layouts and caveats

Typical local layouts are:

For dataset="ICIO":

OECD/ICIO/
├── Extended/
│   └── 2022.csv
└── Regular/
    └── 2022_SML.csv

For dataset="IOT":

OECD/IOT/TTL/
└── ITA2022ttl.csv

For dataset="SUT", path is not required; use country= and year= to select the SDMX slice.

Practical caveats:

  • the IOT workflow supports only the OECD accounts labeled as total table;

  • in the extended ICIO release, CN1 and CN2 are aggregated automatically into CHN, and MX1 and MX2 into MEX.

import mario
Parse one explicit OECD ICIO csv

Choose Regular (2022_SML.csv) or Extended (2022.csv) and pass the exact local file. Both editions have been parsed and checked for every year from 1995 to 2022 in the Database explorer.

Extended separates parts of Chinese and Mexican production in its original files. MARIO sums CN1/CN2 into CHN and MX1/MX2 into MEX, so both editions expose the same national regions and industry classification after parsing. The downloadable set lists describe these parsed sets.

For a directory containing several years, point path to the specific Regular or Extended folder and also pass year=2022. Use calc_all=False when you only need the original matrices and sets.

The browser sums the VA row for value added. GDP also includes TLS on intermediate and final uses; the final-use taxes must be read from the original CSV because MARIO’s V contains industry columns only. These ICIO files are in nominal million USD and do not include an emissions extension.

db = mario.parse_oecd(
    path="/path/to/2022_SML.csv",
    dataset="ICIO",
)

db
Parse one national OECD total-table IOT

Use this when you want one national total table such as CZE2014ttl.csv.

db = mario.parse_oecd(
    path="/path/to/ITA2022ttl.csv",
    dataset="IOT",
)

db
Parse one OECD SUT directly from the SDMX API

In principle, this workflow does not require a local path, but it does require country= and year=.

At the moment we do not show an executable example in the documentation because the official OECD SDMX endpoint can intermittently refuse the request with HTTP errors such as 403 Forbidden. When that happens, the issue is upstream of MARIO’s SUT transformation logic and the parse cannot complete.

For this reason, treat dataset="SUT" as temporarily unstable in the public docs until the OECD SDMX service becomes reliably accessible again.

db = mario.parse_oecd(
    dataset="SUT",
    country="ITA",
    year=2022,
)

db
Environmental extensions

Warning. The OECD parser currently exposes the economic tables only.

ICIO, national IOT, and SUT tables are therefore not parsed as environmentally extended tables in MARIO at the moment.

Continue with Inspect and query a database or Calculate matrices.