IfcCSV

IfcCSV lets you view and edit IFC data using spreadsheets or tabular datasets. IfcCSV supports CSV (Comma Separated Values), ODS (Open Document Spreadsheet), XLSX (Microsoft Excel), Pandas DataFrames, and regular Python lists.

IfcCSV lets you select rooted elements using the IFC selection queries. These elements may be physical elements (walls, doors, windows, etc), construction types (wall types, door types, window types, etc), or even non-geometric (tasks, resources, cost items, etc).

Once you have selected a list of elements, you may specify attributes, properties, quantities, or relationships to extract and use as columns in your table.

For example, you might use a selection query of IfcDoor, for all doors in your project. You may then specify a class attribute, a Name attribute, a type.Name relationship, and a type.Description relationship. You can then produce a spreadsheet like this:

GlobalId	Class	Name	Type Name	Type Description
3AjGVS9EjBeBrDA5_tAcwQ	IfcDoor	01	DT-A	Single swing steel frame door
07BewvHLn2$x6HsHH06rAA	IfcDoor	02	DT-A	Single swing steel frame door
3b3Mk8uIb3Qu_eSPKxsI8x	IfcDoor	01	DT-B	Double swing steel frame fire door
…	…	…	…	…

Note

By default, IfcCSV automatically inserts the GlobalId column at the beginning, in order to uniquely identify the element.

This tabular data may then be exported in your desired format.

You may then edit the data, and reimport the data back into IFC. The changes you make in the spreadsheet or table will also be made in the IFC.

There are different methods of installation, depending on your situation.

1. PyPI is recommended for developers using Pip.

2. Using Bonsai is recommended for non-developers wanting a graphical interface.

3. Source installation is recommended for developers wanting to use the latest code as a library or a CLI utility.

PyPI

pip install ifccsv

Using Bonsai

Bonsai is a Blender Add-on that provides a graphical interface for IfcOpenShell. Other than providing a graphical IFC authoring platform, it also comes with IfcOpenShell, its utilities, and a Python shell built-in. This means you don’t need to install Python first, and you also can compare your IfcOpenShell scripting to what you see with a visual model viewer, or use a graphical interface to access the IfcOpenShell utilities.

1. Install Bonsai by following the Bonsai installation documentation.

2. Launch Blender. Change to the Scene Properties tab in the Properties Panel. Scroll down to the IFC Collaboration > IFC CSV Import / Export panel.

3. Browse to your IFC file.

4. Type in a filter query, such as IfcDoor.

5. Optionally add attributes you’d like to export.

6. Press Export IFC to CSV

TODO: add pictures and make this clearer for non-developers.

Source installation

1. Install IfcOpenShell

2. Clone the source code.

3. cd /path/to/IfcOpenShell/src/ifccsv

Depending on which formats you want to edit, you will need to install more dependencies:

• pip install odfpy for ODS support

• pip install openpyxl for XLSX support

• pip install pandas for ODS, XLSX, and Pandas DataFrame support

Here is a minimal example of how to use IfcDiff as a Python module or CLI utility:

$ python -m ifccsv -h
usage: ifccsv.py [-h] -i IFC [-s SPREADSHEET] [-f FORMAT] [-d DELIMITER] [-n NULL] [-e EMPTY] [--bool_true BOOL_TRUE] [--bool_false BOOL_FALSE] [--concat CONCAT] [-q QUERY] [-a ATTRIBUTES [ATTRIBUTES ...]]
                 [--headers HEADERS [HEADERS ...]] [--sort SORT [SORT ...]] [--order ORDER [ORDER ...]] [--export] [--import]

Exports IFC data to and from CSV

options:
  -h, --help            show this help message and exit
  -i IFC, --ifc IFC     The IFC file
  -s SPREADSHEET, --spreadsheet SPREADSHEET
                        The spreadsheet file
  -f FORMAT, --format FORMAT
                        The format, chosen from csv, ods, or xlsx
  -d DELIMITER, --delimiter DELIMITER
                        The delimiter in CSV. Defaults to a comma.
  -n NULL, --null NULL  How to represent null values. Defaults to N/A.
  -e EMPTY, --empty EMPTY
                        How to represent empty strings. Defaults to a hyphen.
  --bool_true BOOL_TRUE
                        How to represent true values. Defaults to YES.
  --bool_false BOOL_FALSE
                        How to represent false values. Defaults to NO.
  --concat CONCAT       How to concatenate lists. Defaults to ', '.
  -q QUERY, --query QUERY
                        Specify a IFC query selector, such as "IfcWall"
  -a ATTRIBUTES [ATTRIBUTES ...], --attributes ATTRIBUTES [ATTRIBUTES ...]
                        Specify attributes that are part of the extract, using the IfcQuery syntax such as 'class', 'Name' or 'Pset_Foo.Bar'
  --headers HEADERS [HEADERS ...]
                        Specify human readable headers that correlate to each attribute.
  --sort SORT [SORT ...]
                        Specify one or more attributes to sort by.
  --order ORDER [ORDER ...]
                        Choose the sort order from ASC or DESC for each sorted attribute.
  --export              Export from IFC to the desired format.
  --import              Import from the autodetected format to IFC.
$ python -m ifccsv -i model.ifc -s out.csv -f csv -q IfcProduct -a "Name" "Description" --export
$ cat out.csv

Here is a minimal example of how to use IfcCSV as a library:

import ifcopenshell
from ifccsv import IfcCsv

model = ifcopenshell.open("/path/to/model.ifc")
# Using the selector is optional. You may specify elements as a list manually if you prefer.
# e.g. elements = model.by_type("IfcElement")
elements = ifcopenshell.util.selector.filter_elements(model, "IfcElement")
attributes = ["Name", "Description"]

# Export our model's elements and their attributes to a CSV.
ifc_csv = IfcCsv()
ifc_csv.export(model, elements, attributes, output="out.csv", format="csv", delimiter=",", null="-")

# Optionally, you can explicitly export to different formats.
# ifc_csv = IfcCsv()
# ifc_csv.export(model, elements, attributes)
ifc_csv.export_csv("out.csv", delimiter=";")
ifc_csv.export_ods("out.ods")
ifc_csv.export_xlsx("out.xlsx")

# Optionally, you can create a Pandas DataFrame.
df = ifc_csv.export_pd()
print(df)

# Optionally, you can directly fetch the headers and rows as Python lists.
print(ifc_csv.headers)
print(ifc_csv.results)

# You can also import changes from a CSV
ifc_csv.Import(model, "input.csv")
model.write("/path/to/updated_model.ifc")