Data Import and Export
PyPSA is intended to be data format agnostic, but given the reliance internally on pandas DataFrames, it is natural to use comma-separated-variable (CSV) files.
Import from folder of CSV files
Create a folder with CSVs for each component type
generators.csv, storage_units.csv), then a CSV for each
time-dependent variable (e.g.
loads-p_set.csv). Then run
It is not necessary to add every single column, only those where values differ from the defaults listed in Components. All empty values/columns are filled with the defaults.
Export to folder of CSV files
The network can be exported as a folder of csv files with
Adding and removing components one-by-one
Networks can also be built step-by-step for each component by calling
pypsa.Network.add(). Likewise, components can also be removed with
Adding and removing multiple components
Multiple components can be added by calling
pypsa.Network.madd(). Multiple components can be removed by calling
Adding components using pandas DataFrames
To add multiple components whose static attributes are given in a
pandas DataFrame, use
To import time-varying information use
Export to netCDF
netCDF files take up less space than CSV files and are faster to load.
netCDF is also preferred over HDF5 because netCDF is structured more cleanly, is easier to use from other programming languages, can limit float precision to save space and supports lazy loading.
To export network and components to a netCDF file run
Import from netCDF
To import network data from netCDF file run
Export to HDF5
netCDF is preferred over HDF5 because netCDF is structured more cleanly, is easier to use from other programming languages, can limit float precision to save space and supports lazy loading.
To export network and components to an HDF store run
Import from HDF5
To import network data from HDF5 store at
Import from Pypower
PyPSA supports import from Pypower’s ppc dictionary/numpy.array format
version 2, see
Import from Pandapower
Importing from pandapower is still in beta; not all pandapower data is supported.
PyPSA supports import from pandapower using the function