API Reference

API references for all modules

Data input and output

Test system

load_test() Returns a small test MRIO

Download MRIO databases

Download publicly EE MRIO databases from the web. This is currently implemented for the WIOD and OECD_ICIO database (EXIOBASE and EORA26 require registration before downloading).

download_exiobase3(storage_folder[, years, …]) Downloads EXIOBASE 3 files from Zenodo
download_wiod2013(storage_folder[, years, …]) Downloads the 2013 wiod release
download_oecd(storage_folder[, version, …]) Downloads the OECD ICIO tables

Raw data

parse_exiobase1(path) Parse the exiobase1 raw data files.
parse_exiobase2(path[, charact, popvector]) Parse the exiobase 2.2.2 source files for the IOSystem
parse_exiobase3(path) Parses the public EXIOBASE 3 system
generic_exiobase12_parser(exio_files[, system]) Generic EXIOBASE version 1 and 2 parser
parse_wiod(path[, year, names, popvector]) Parse the wiod source files for the IOSystem
parse_eora26(path[, year, price, country_names]) Parse the Eora26 database
parse_oecd(path[, year]) Parse the OECD ICIO tables

Save data

Currently, the full MRIO system can be saved in txt or the python specific binary format (‘pickle’). Both formats work with the same API interface:

IOSystem.save(path[, table_format, sep, …]) Saving the system to path
IOSystem.save_all(path[, table_format, sep, …]) Saves the system and all extensions

Already saved MRIO databases can be archived with

archive(source, archive[, path_in_arc, …]) Archives a MRIO database as zip file

Load processed data

This functions load IOSystems or individual extensions which have been saved with pymrio before.

load(path[, include_core, path_in_arc]) Loads a IOSystem or Extension previously saved with pymrio
load_all(path[, include_core, subfolders, …]) Loads a full IO system with all extension in path


pymrio stores all tables as pandas DataFrames. This data can be accessed with the usual pandas methods. On top of that, the following functions return (in fact yield) several tables at once:

IOSystem.get_DataFrame([data, with_unit, …]) Yields all panda.DataFrames or there names
IOSystem.get_extensions([data]) Yields the extensions or their names

For the extensions, it is also possible to receive all data (F, S, M, D_cba, …) for one specified row.

Extension.get_row_data(row[, name]) Returns a dict with all available data for a row in the extension

Exploring the IO System

The following functions provide informations about the structure of the IO System and the extensions. The methods work on the IOSystem as well as directly on the Extensions.

IOSystem.get_regions([entries]) Returns the names of regions in the IOSystem as unique names in order
IOSystem.get_sectors([entries]) Names of sectors in the IOSystem as unique names in order
IOSystem.get_Y_categories([entries]) Returns names of y cat.
IOSystem.get_index([as_dict, grouping_pattern]) Returns the index of the DataFrames in the system
IOSystem.set_index(index) Sets the pd dataframe index of all dataframes in the system to index
Extension.get_rows() Returns the name of the rows of the extension


Top level methods

The top level level function calc_all checks the IO System and its extensions for missing parts and calculate these. This function calls the specific calculation method for the core system and for the extensions.

IOSystem.calc_all() Calculates missing parts of the IOSystem and all extensions.
IOSystem.calc_system() Calculates the missing part of the core IOSystem
Extension.calc_system(x, Y[, Y_agg, L, …]) Calculates the missing part of the extension plus accounts

Low level matrix calculations

The top level functions work by calling the following low level functions. These can also be used independently from the IO System for pandas DataFrames and numpy array.

calc_x(Z, Y) Calculate the industry output x from the Z and Y matrix
calc_Z(A, x) calculate the Z matrix (flows) from A and x
calc_A(Z, x) Calculate the A matrix (coefficients) from Z and x
calc_L(A) Calculate the Leontief L from A
calc_S(F, x) Calculate extensions/factor inputs coefficients
calc_F(S, x) Calculate total direct impacts from the impact coefficients
calc_M(S, L) Calculate multipliers of the extensions
calc_e(M, Y) Calculate total impacts (footprints of consumption Y)
calc_accounts(S, L, Y, nr_sectors) Calculate sector specific cba and pba based accounts, imp and exp accounts

Metadata and history recording

Each pymrio core system object contains a field ‘meta’ which stores meta data as well as changes to the MRIO system. This data is stored as json file in the root of a saved MRIO data and accessible through the attribute ‘.meta’.

MRIOMetaData([location, description, name, …])
MRIOMetaData.note(entry) Add the passed string as note to the history
MRIOMetaData.history All recorded history
MRIOMetaData.modification_history All modification history entries
MRIOMetaData.note_history All note history entries
MRIOMetaData.file_io_history All fileio history entries
MRIOMetaData.save([location]) Saves the current status of the metadata

Modifiying the IO System and its Extensions


The IO System method ‘aggregate’ accepts concordance matrices and/or aggregation vectors. The latter can be generated automatically for various aggregation levels for the test system and EXIOBASE 2.

IOSystem.aggregate([region_agg, sector_agg, …]) Aggregates the IO system.
build_agg_vec(agg_vec, **source) Builds an combined aggregation vector based on various classifications

Characterizing stressors

Extension.characterize(factors[, …]) Characterize stressors

Analysing the source of impacts

Extension.diag_stressor(stressor[, name, _meta]) Diagonalize one row of the stressor matrix for a flow analysis.

Changing extensions

IOSystem.remove_extension([ext]) Remove extension from IOSystem
concate_extension(*extensions, name) Concatenate extensions
parse_exio12_ext(ext_file, index_col, name) Parse an EXIOBASE version 1 or 2 like extension file into pymrio.Extension


IOSystem.rename_regions(regions) Sets new names for the regions
IOSystem.rename_sectors(sectors) Sets new names for the sectors
IOSystem.rename_Y_categories(Y_categories) Sets new names for the Y_categories


The following method works on the IO System (generating reports for every extension available) or at individual extensions.

IOSystem.report_accounts(path[, per_region, …]) Generates a report to the given path for all extension


Extension.plot_account(row[, per_capita, …]) Plots D_pba, D_cba, D_imp and D_exp for the specified row (account)


IOSystem.reset_to_flows([force]) Keeps only the absolute values.
IOSystem.reset_to_coefficients() Keeps only the coefficient.
IOSystem.copy([new_name]) Returns a deep copy of the system