Python 2.7 script that converts a life cycle inventory (LCI) from Excel into Simapro CSV format
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LCAscript_v1.2.py
LCI1.csv
LCI2.csv
LCI_example.xlsx
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README.md
spcsv.py

README.md

Simapro-CSV-converter

Python 2.7 script that converts a life cycle inventory (LCI) from Excel into Simapro CSV format
by Massimo (2016)

What the script does:

  • convert multiple sheets at once (create a .csv file for each sheet in the Excel file)
  • both database processes and all types of exchanges can be specified

What it doesn't do:

  • specify sub-compartment of emission (e.g. "high-population")
  • specify uncertainty
  • add comments

(all these parameters can be specified directly in the .csv file though)

Requirements:

Instructions:

  1. Prepare the life cycle inventory in Excel, save it in the same folder as the Python script
  2. From shell, navigate into the folder and type python script_name myfile_name
  3. From SimaPro, use Import>File and the following settings:
    • File format: "SimaPro CSV"
    • Object link method: "Try to link imported objects to existing objects first"
    • CSV format separator: "Tab"
    • Other options: "Replace existing processes..."

Compiling LCIs in Excel and worked example:

The Excel file attached includes two fictional LCIs. From shell, typing python LCAscript_v1.2.py LCI_Example.xlsx returns the two files LCI1.csv and LCI2.csv to be imported into SimaPro.

  • Cells A1:D6 are fixed, do not insert rows or columns there
  • Each column is a process of the foreground system, and is matched by an identically named row and in the same order (see that E7:G9 in the example is a square matrix)
  • Use exact LCI database process names under the foreground system
  • Use "Raw", "Air", "Water", "Soil", "Waste", "Social", "Economic" to indicate exchanges
  • Use "Wastetotreatment" to indicate database processes of the waste treatment category

UPDATE 2017: Python3 function to convert a pd.dataframe object in simapro.csv file

Save the spcsv.py file in your working directory and use the to_spcsv function in this way:

import pandas as pd
from spcsv import *

spcsv_ready = pd.read_excel('LCI_Example.xlsx', 'LCI1', index = False, header = None)

to_spcsv(spcsv_ready, 'LCI1.csv')

Of course one can create his own dataframe directly in python (maybe I'll upload a tutorial at some point) with the same structure of the one above...or change the spcsv.py code to reflect other structures.

UPDATE 2018

Works with python3 too...