class Pandas[source]


Combines all data arrays into a Pandas DataFrame object

The structure of a DataFrame is a very close match to OpenPNMs data storage. Each key becomes a column header in the Dataframe, and each pore or throat entry becomes a row.

Limitations of the DataFrame are the inability to have multidimensional data in a single column. The methods on a DataFrame are also oriented towards time-series data.

Nonetheless, Pandas offers many useful features such as performing statistical analysis on property. DataFrames also offer many options for exporting to other file formats, so if a format is not yet supported by OpenPNM, this could be an solution.

classmethod export_data(network=None, phases=[], join=False, delim=' | ')[source]

Convert the Network (and optionally Phase) data to Pandas DataFrames.

  • network (OpenPNM Network Object) – The network containing the data to be stored

  • phases (list of OpenPNM Phase Objects) – The data on each supplied phase will be added to DataFrame

  • join (boolean) – If False (default), two DataFrames are returned with pore data in one, and throat data in the other. If True the pore and throat data are combined into a single DataFrame. This can be problematic as it will put NaNs into all the pore columns which are shorter than the throat columns.


  • Pandas DataFrame object containing property and label data in each

  • column. If join was False (default) the two DataFrames are

  • returned i a named tuple, or else a single DataFrame with pore and

  • throat data in the same file, despite the column length being

  • different.