formulas: An Excel formulas interpreter in Python.

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release: 1.2.2
date: 2022-01-22 00:00:00
keywords: excel, formulas, interpreter, compiler, dispatch
license: EUPL 1.1+

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.


To install it use (with root privileges):

$ pip install formulas

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

$ python install

Install extras

Some additional functionality is enabled installing the following extras:

To install formulas and all extras, do:

$ pip install formulas[all]

Development version

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

$ pip install

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'}

    >>> 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'}

    >>> 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')

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


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

    >>> 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'!D2", <Ranges>('[excel.xlsx]DATA'!D2)=[[1]]),
          ("'[excel.xlsx]'!INPUT_B", <Ranges>('[excel.xlsx]DATA'!A3)=[[6]]),
          ("'[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

    >>> 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(...))])

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

.. dispatcher:: dsp

    >>> 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)

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()

>>> 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.xlsx]DATA'!C1": "outputs",
 "'[excel.xlsx]DATA'!D1": "defaults",
 "'[excel.xlsx]DATA'!A2": 2,
 "'[excel.xlsx]DATA'!D2": 1,
 "'[excel.xlsx]DATA'!A3": 6,
 "'[excel.xlsx]DATA'!A4": 5,
 "'[excel.xlsx]DATA'!B2": "=('[excel.xlsx]DATA'!A2 + '[excel.xlsx]DATA'!A3)",
 "'[excel.xlsx]DATA'!C2": "=(('[excel.xlsx]DATA'!B2 / '[excel.xlsx]DATA'!B3) + '[excel.xlsx]DATA'!D2)",
 "'[excel.xlsx]DATA'!B3": "=('[excel.xlsx]DATA'!B2 - '[excel.xlsx]DATA'!A3)",
 "'[excel.xlsx]DATA'!C3": "=(('[excel.xlsx]DATA'!C2 * '[excel.xlsx]DATA'!A2) + '[excel.xlsx]DATA'!D3)",
 "'[excel.xlsx]DATA'!D3": "=(1 + '[excel.xlsx]DATA'!D2)",
 "'[excel.xlsx]DATA'!B4": "=MAX('[excel.xlsx]DATA'!A3:A4, '[excel.xlsx]DATA'!B2)",
 "'[excel.xlsx]DATA'!C4": "=(('[excel.xlsx]DATA'!B3 ^ '[excel.xlsx]DATA'!C2) + '[excel.xlsx]DATA'!D4)",
 "'[excel.xlsx]DATA'!D4": "=(1 + '[excel.xlsx]DATA'!D3)"
>>> xl_json = json.dumps(xl_dict, indent=True)  # To JSON.
>>> xl_model = formulas.ExcelModel().from_dict(json.loads(xl_json))  # From JSON.

Custom functions

An example how to add a custom function to the formula parser is the following:

>>> import formulas
>>> FUNCTIONS = formulas.get_functions()
>>> FUNCTIONS['MYFUNC'] = lambda x, y: 1 + y + x
>>> func = formulas.Parser().ast('=MYFUNC(1, 2)')[1].compile()
>>> func()

Next moves

Things yet to do: implement the missing Excel formulas.


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