.. _start-quick:

################################################## formulas: An Excel formulas interpreter in Python. ################################################## |pypi_ver| |test_status| |cover_status| |docs_status| |downloads| |month_downloads| |github_issues| |python_ver| |proj_license| |binder|

:release: 1.3.4 :date: 2026-03-11 18:35:00 :repository: https://github.com/vinci1it2000/formulas :pypi-repo: https://pypi.org/project/formulas/ :docs: http://formulas.readthedocs.io/ :wiki: https://github.com/vinci1it2000/formulas/wiki/ :download: http://github.com/vinci1it2000/formulas/releases/ :donate: https://donorbox.org/formulas :keywords: excel, formulas, interpreter, compiler, dispatch :developers: .. include:: AUTHORS.rst :license: EUPL 1.1+ <https://joinup.ec.europa.eu/software/page/eupl>_

.. _start-pypi: .. _start-intro:

What is formulas?

formulas implements an interpreter for Excel formulas, which parses and compile Excel formulas expressions.

Moreover, it compiles Excel workbooks to python and executes without using the Excel COM server. Hence, Excel is not needed.

Installation

To install it use (with root privileges):

.. code-block:: console

$ pip install formulas

Or download the last git version and use (with root privileges):

.. code-block:: console

$ python setup.py install

Install extras

Some additional functionality is enabled installing the following extras:

• excel: enables to compile Excel workbooks to python and execute using: :class:~formulas.excel.ExcelModel.
• plot: enables to plot the formula ast and the Excel model.

To install formulas and all extras, do:

.. code-block:: console

$ pip install formulas[all]

Development version

To help with the testing and the development of formulas, you can install the development version:

.. code-block:: console

$ pip install https://github.com/vinci1it2000/formulas/archive/dev.zip

CLI Quickstart

The formulas command-line interface works with spreadsheet models and accepts .xlsx, .ods, and .json inputs.

A typical workflow starts by calculating a workbook. You can override input values directly from the command line and request specific cells to be rendered in the output.

.. code-block:: console

$ formulas calc test/test_files/excel.xlsx \
    --overwrite "'[excel.xlsx]'!INPUT_A=3" \
    --overwrite "'[excel.xlsx]DATA'!B3=1" \
    --render "'[excel.xlsx]DATA'!C2=result" \
    --output-format json

Spreadsheet models can also be converted into a portable JSON representation. This is useful when the model needs to be versioned, inspected, or executed without the original workbook.

.. code-block:: console

$ formulas build test/test_files/excel.xlsx \
    --output-file model.json

For validation purposes, a workbook can be tested directly from the CLI. The following command runs the tests and prints a short summary.

.. code-block:: console

$ formulas test test/test_files/excel.xlsx --summary

Finally, a model can be exposed as a lightweight HTTP API, allowing other applications to execute it remotely. The serve command requires the optional web dependencies (pip install formulas[web]).

.. code-block:: console

$ formulas serve test/test_files/excel.xlsx \
    --host 127.0.0.1 \
    --port 5000

Each command provides additional options and examples through the built-in help system:

.. code-block:: console

$ formulas COMMAND --help

.. _end-quick:

Basic Examples

The following sections will show how to:

• parse a Excel formulas;
• load, compile, and execute a Excel workbook;
• extract a sub-model from a Excel workbook;
• add a custom function.

Parsing formula

An example how to parse and execute an Excel formula is the following:

>>> import formulas
>>> func = formulas.Parser().ast('=(1 + 1) + B3 / A2')[1].compile()

To visualize formula model and get the input order you can do the following:

.. dispatcher:: func :opt: graph_attr={'ratio': '1'} :code:

>>> list(func.inputs)
['A2', 'B3']
>>> func.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap({=((1 + 1) + (B3 / A2)): SiteMap({})})

Finally to execute the formula and plot the workflow:

.. dispatcher:: func :opt: workflow=True, graph_attr={'ratio': '1'} :code:

>>> func(1, 5)
Array(7.0, dtype=object)
>>> func.plot(workflow=True, view=False)  # Set view=True to plot in the default browser.
SiteMap({=((1 + 1) + (B3 / A2)): SiteMap({})})

Excel workbook

An example how to load, calculate, and write an Excel workbook is the following:

.. testsetup::

>>> import os.path as osp
>>> from setup import mydir
>>> fpath = osp.join(mydir, 'test/test_files/excel.xlsx')
>>> dir_output = osp.join(mydir, 'test/test_files/tmp')

.. doctest::

>>> import formulas
>>> fpath, dir_output = 'excel.xlsx', 'output'  # doctest: +SKIP
>>> xl_model = formulas.ExcelModel().loads(fpath).finish()
>>> xl_model.calculate()
Solution(...)
>>> xl_model.write(dirpath=dir_output)
{'EXCEL.XLSX': {Book: <openpyxl.workbook.workbook.Workbook ...>}}

.. tip:: If you have or could have circular references, add circular=True to finish method.

To plot the dependency graph that depict relationships between Excel cells:

.. dispatcher:: dsp :code:

>>> dsp = xl_model.dsp
>>> dsp.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap({ExcelModel: SiteMap(...)})

To overwrite the default inputs that are defined by the excel file or to impose some value to a specific cell:

>>> xl_model.calculate(
...     inputs={
...         "'[excel.xlsx]'!INPUT_A": 3,  # To overwrite the default value.
...         "'[excel.xlsx]DATA'!B3": 1  # To impose a value to B3 cell.
...     },
...     outputs=[
...        "'[excel.xlsx]DATA'!C2", "'[excel.xlsx]DATA'!C4"
...     ] # To define the outputs that you want to calculate.
... )
 Solution({"'[excel.xlsx]'!INPUT_A": <Ranges>('[excel.xlsx]DATA'!A2)=[[3]],
           "'[excel.xlsx]DATA'!B3": <Ranges>('[excel.xlsx]DATA'!B3)=[[1]],
           "'[excel.xlsx]DATA'!A2": <Ranges>('[excel.xlsx]DATA'!A2)=[[3]],
           "'[excel.xlsx]DATA'!A3": <Ranges>('[excel.xlsx]DATA'!A3)=[[6]],
           "'[excel.xlsx]DATA'!A4": <Ranges>('[excel.xlsx]DATA'!A4)=[[5]],
           "'[excel.xlsx]DATA'!D2": <Ranges>('[excel.xlsx]DATA'!D2)=[[1]],
           "'[excel.xlsx]'!INPUT_B": <Ranges>('[excel.xlsx]DATA'!A3)=[[6]],
           "'[excel.xlsx]'!INPUT_C": <Ranges>('[excel.xlsx]DATA'!A4)=[[5]],
           "'[excel.xlsx]DATA'!A3:A4": <Ranges>('[excel.xlsx]DATA'!A3:A4)=[[6] [5]],
           "'[excel.xlsx]DATA'!B2": <Ranges>('[excel.xlsx]DATA'!B2)=[[9.0]],
           "'[excel.xlsx]DATA'!D3": <Ranges>('[excel.xlsx]DATA'!D3)=[[2.0]],
           "'[excel.xlsx]DATA'!C2": <Ranges>('[excel.xlsx]DATA'!C2)=[[10.0]],
           "'[excel.xlsx]DATA'!D4": <Ranges>('[excel.xlsx]DATA'!D4)=[[3.0]],
           "'[excel.xlsx]DATA'!C4": <Ranges>('[excel.xlsx]DATA'!C4)=[[4.0]]})

To build a single function out of an excel model with fixed inputs and outputs, you can use the compile method of the ExcelModel that returns a DispatchPipe_. This is a function where the inputs and outputs are defined by the data node ids (i.e., cell references).

.. dispatcher:: func :code:

>>> func = xl_model.compile(
...     inputs=[
...         "'[excel.xlsx]'!INPUT_A",  # First argument of the function.
...         "'[excel.xlsx]DATA'!B3"   # Second argument of the function.
...     ], # To define function inputs.
...     outputs=[
...         "'[excel.xlsx]DATA'!C2", "'[excel.xlsx]DATA'!C4"
...     ] # To define function outputs.
... )
>>> func
<schedula.utils.dsp.DispatchPipe object at ...>
>>> [v.value[0, 0] for v in func(3, 1)]  # To retrieve the data.
[10.0, 4.0]
>>> func.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap({ExcelModel: SiteMap(...)})

.. _DispatchPipe: https://schedula.readthedocs.io/en/master/_build/schedula/utils/dsp/schedula.utils.dsp.DispatchPipe.html#schedula.utils.dsp.DispatchPipe

Alternatively, to load a partial excel model from the output cells, you can use the from_ranges method of the ExcelModel:

.. dispatcher:: dsp :code:

>>> xl = formulas.ExcelModel().from_ranges(
...     "'[%s]DATA'!C2:D2" % fpath,  # Output range.
...     "'[%s]DATA'!B4" % fpath,  # Output cell.
... )
>>> dsp = xl.dsp
>>> sorted(dsp.data_nodes)
["'[excel.xlsx]'!INPUT_A",
 "'[excel.xlsx]'!INPUT_B",
 "'[excel.xlsx]'!INPUT_C",
 "'[excel.xlsx]DATA'!A2",
 "'[excel.xlsx]DATA'!A3",
 "'[excel.xlsx]DATA'!A3:A4",
 "'[excel.xlsx]DATA'!A4",
 "'[excel.xlsx]DATA'!B2",
 "'[excel.xlsx]DATA'!B3",
 "'[excel.xlsx]DATA'!B4",
 "'[excel.xlsx]DATA'!C2",
 "'[excel.xlsx]DATA'!D2"]

JSON export/import

The `ExcelModel` can be exported/imported to/from a readable JSON format. The
reason of this functionality is to have format that can be easily maintained
(e.g. using version control programs like `git`). Follows an example on how to
export/import to/from JSON an `ExcelModel`:

.. testsetup::

    >>> import formulas
    >>> import os.path as osp
    >>> from setup import mydir
    >>> fpath = osp.join(mydir, 'test/test_files/excel.xlsx')
    >>> xl_model = formulas.ExcelModel().loads(fpath).finish()

.. doctest::

    >>> import json
    >>> xl_dict = xl_model.to_dict()  # To JSON-able dict.
    >>> xl_dict  # Exported format. # doctest: +SKIP
    {
     "'[excel.xlsx]DATA'!A1": "inputs",
     "'[excel.xlsx]DATA'!B1": "Intermediate",
     "'[excel.x