Skip to main content

Parse and compile Excel formulas and workbooks in python code.

Project description

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):

$ pip install formulas

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

$ 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: ExcelModel.

  • plot: enables to plot the formula ast and the Excel model.

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 https://github.com/vinci1it2000/formulas/archive/dev.zip

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:

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

[graph]

Finally to execute the formula and plot the workflow:

>>> 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())])

[graph]

Excel workbook

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

>>> import formulas
>>> fpath, dir_output = 'excel.xlsx', 'output'
>>> 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:

>>> dsp = xl_model.dsp
>>> dsp.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap([(ExcelModel, SiteMap(...))])

[graph]

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).

>>> 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(...))])

[graph]

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

>>> 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"]

[graph]

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:

>>> import json
>>> xl_dict = xl_model.to_dict()  # To JSON-able dict.
>>> xl_dict  # Exported format.
{
 "'[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()
4

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

formulas-1.2.0.tar.gz (247.5 kB view details)

Uploaded Source

Built Distribution

formulas-1.2.0-py2.py3-none-any.whl (59.0 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file formulas-1.2.0.tar.gz.

File metadata

  • Download URL: formulas-1.2.0.tar.gz
  • Upload date:
  • Size: 247.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for formulas-1.2.0.tar.gz
Algorithm Hash digest
SHA256 71d5fe13a8e0d815d7fced7a935831b6805cc710b7c360fb39f317f83070ef76
MD5 0990a2b507c147d8770a6f208553f2bc
BLAKE2b-256 2440894ca2dcea210768f75c040f2588ea2a7e569c14a7c484c8ce3a26ac94d1

See more details on using hashes here.

File details

Details for the file formulas-1.2.0-py2.py3-none-any.whl.

File metadata

  • Download URL: formulas-1.2.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 59.0 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for formulas-1.2.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 2ca3ab4c69e5fc2fb992a6ca498ac88744e366f792feedf88c1016afaad0ea96
MD5 3631afa105ac0414f2e1bb33d4522cec
BLAKE2b-256 52c38427527224ecbb2cdaffafdbd4fa0a9d8fc39e4a2a193c426ec11c85a5ae

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page