Import from Excel#

Authors

This notebook was prepared in October 2023 by @michaelweinold.

Download

You can download this notebook here: import_excel.ipynb
You can download the required Excel sheet here: my_own_database.xlsx

Prerequisites

A working installation of Brightway25.
The Ecoinvent 3.9.1, cutoff database added to your Brightway project (here named import_excel).

# Brightway packages
import bw2io as bi
import bw2data as bd
import bw2calc as bc

bd.projects.set_current("import_excel")

Here, we assume you have added the Ecoinvent 3.9.1 database to your Brightway project.

bd.databases

Databases dictionary with 2 object(s):
	ecoinvent-3.9.1-biosphere
	ecoinvent-3.9.1-cutoff

We can now import an Excel sheet into Brightway. This is useful if you have data in Excel format that you want to use in your LCA calculations.

Creating such an Excel sheet is simple: Follow the basic example in my_own_database.xlsx and add your own data.

As you can see, you must separate Activity blocks by a single empty row. The name of the individual sheets does not matter. Brightway attempts to import all sheets in the Excel file. In the example case, there is only a single sheet, named data. You may stop Brightway from importing specific sheets by writing skip in the first cell of the sheet.

my_db = bi.ExcelImporter("my_own_database.xlsx")

Extracted 1 worksheets in 0.01 seconds

my_db.apply_strategies()

Applying strategy: csv_restore_tuples
Applying strategy: csv_restore_booleans
Applying strategy: csv_numerize
Applying strategy: csv_drop_unknown
Applying strategy: csv_add_missing_exchanges_section
Applying strategy: normalize_units
Applying strategy: normalize_biosphere_categories
Applying strategy: normalize_biosphere_names
Applying strategy: strip_biosphere_exc_locations
Applying strategy: set_code_by_activity_hash
Applying strategy: link_iterable_by_fields
Applying strategy: assign_only_product_as_production
Applying strategy: link_technosphere_by_activity_hash
Applying strategy: drop_falsey_uncertainty_fields_but_keep_zeros
Applying strategy: convert_uncertainty_types_to_integers
Applying strategy: convert_activity_parameters_to_list
Applied 16 strategies in 5.50 seconds

my_db.match_database(
    "ecoinvent-3.9.1-cutoff", fields=["name", "unit", "location", "reference product"]
)

Applying strategy: link_iterable_by_fields

my_db.statistics()

2 datasets
	4 exchanges
	Links to the following databases:
		my_own_database (3 exchanges)
		ecoinvent-3.9.1-cutoff (1 exchanges)
	0 unlinked exchanges (0 types)
		

(2, 4, 0, 0)

my_db.write_database()

100%|██████████| 2/2 [00:00<00:00, 5461.33it/s]

Vacuuming database 
Created database: my_own_database

bd.databases

Databases dictionary with 3 object(s):
	ecoinvent-3.9.1-biosphere
	ecoinvent-3.9.1-cutoff
	my_own_database

We can now browse the activities in the database…

[activitiy for activitiy in bd.Database("my_own_database")]

['magic ingredient production' (kilogram, CO, None),
 'magic banana production' (kilogram, CO, None)]

…and conduct a basic life-cycle assessment of our new activity:

act

'magic banana production' (kilogram, CO, None)

banana_activity = [
    activitiy for activitiy in bd.Database("my_own_database")
    if 'magic banana' in activitiy['name']
]

ipcc_gwp_method = [
    method for method in bd.methods
    if 'IPCC 2021' == method[1]
    and 'climate change' == method[2]
    and '(GWP100)' in method[3]
][0]

my_functional_unit, data_objs, _ = bd.prepare_lca_inputs(
    {act: 42},
    method=ipcc_gwp_method)
my_lca = bc.LCA(demand=my_functional_unit, data_objs=data_objs)
my_lca.lci()
my_lca.lcia()

my_lca.score

0.016015050756031914

Import from Excel#

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