excel-grapher

Build and analyze dependency graphs from Excel workbooks, evaluate formulas with Excel semantics, and export standalone Python code.

Documentation: https://teal-insights.github.io/excel-grapher/

Why this exists

  • Transpilation support: trace formula dependencies to enable Excel → Python translation.
  • Interpretability: visualize and sanity-check spreadsheet logic (GraphViz, Mermaid, NetworkX).
  • Performance-minded: focuses on targeted dependency closure from specific output cells/ranges.
  • Excel semantics in Python: run workbook logic in-process with a full Excel-like evaluator.
  • Exportable: emit standalone Python packages that embed only the runtime surface you need.

Library layout

The unified distribution is excel-grapher and exposes a single import package, excel_grapher, with five main subpackages:

  • excel_grapher/core/ — shared semantic types, coercions, and scalar operators.
  • excel_grapher/runtime/ — Excel-equivalent function implementations and runtime semantics.
  • excel_grapher/grapher/ — workbook loading, graph extraction, Excel write-back, and visualization logic.
  • excel_grapher/evaluator/ — FormulaEvaluator: an Excel emulator for recomputing formulas in the extracted graph in Python.
  • excel_grapher/exporter/ — CodeGenerator: an transpiler for exporting the extracted graph as a standalone Python library.

Typical imports:

from excel_grapher.grapher import create_dependency_graph, DependencyGraph
from excel_grapher.evaluator import FormulaEvaluator
from excel_grapher.exporter import CodeGenerator
from excel_grapher.core import XlError  # and other shared types, if needed

Installation

Released under the MIT license. Install from GitHub:

Using uv (recommended):

# Basic install (NumPy-free)
uv add git+https://github.com/Teal-Insights/excel-grapher

# With vectorized operator / SUMPRODUCT acceleration
uv add "excel-grapher[fast] @ git+https://github.com/Teal-Insights/excel-grapher"

# With NetworkX support
uv add "excel-grapher[networkx] @ git+https://github.com/Teal-Insights/excel-grapher"

# With all optional dependencies (includes `fast`)
uv add "excel-grapher[all] @ git+https://github.com/Teal-Insights/excel-grapher"

Using pip:

pip install git+https://github.com/Teal-Insights/excel-grapher

# With extras:
pip install "excel-grapher[fast] @ git+https://github.com/Teal-Insights/excel-grapher"
pip install "excel-grapher[networkx] @ git+https://github.com/Teal-Insights/excel-grapher"
pip install "excel-grapher[all] @ git+https://github.com/Teal-Insights/excel-grapher"

The default install is correct without NumPy. Install the fast extra when evaluating large workbooks and you want vectorized operator / SUMPRODUCT acceleration. Exported standalone code stays NumPy-free either way.

High-level usage

The library supports a three-stage pipeline, plus Excel write-back of the same graph views used for Python export:

  1. Build a dependency graph from an Excel workbook (excel_grapher.grapher).
  2. Evaluate formulas with Excel semantics over that graph (excel_grapher.evaluator.FormulaEvaluator).
  3. Export standalone Python (excel_grapher.exporter.CodeGenerator) and/or a new .xlsx from the same GraphReadView (excel_grapher.grapher.write_workbook).

A minimal end‑to‑end example:

from pathlib import Path

from excel_grapher.grapher import create_dependency_graph, write_workbook
from excel_grapher.evaluator import FormulaEvaluator
from excel_grapher.exporter import CodeGenerator

workbook_path = Path("model.xlsx")
targets = ["Sheet1!A10"]

# 1) Build a dependency graph
graph = create_dependency_graph(workbook_path, targets, load_values=True)
print(len(graph))  # number of visited nodes

# 2) Evaluate with Excel semantics
with FormulaEvaluator(graph) as ev:
    results = ev.evaluate(targets)

# 3a) Export standalone Python code
code = CodeGenerator(graph).generate(targets)

# 3b) Write a new workbook from the graph (or a ProjectionResult)
write_workbook(graph, Path("edited.xlsx"))

Series bindings

Optional sidecar manifests (.bindings.yaml) declare structured input/output APIs for exported code — set_* and compute_* functions over Records. Validate sidecars from the shell with excel-grapher bindings validate. See the Series bindings guide and Code export guide.

User guide

Detailed documentation lives in the User Guide:

Topic Page
Dependency graphs Read guide
Visualization Read guide
Formula evaluation Read guide
End-to-end demo Read guide
Series bindings Read guide
Code export Read guide
Parity testing Read guide
Contributing Read guide

Examples

Hands-on walkthroughs (Markdown on GitHub):

Topic Walkthrough
Graph extraction extraction_basics.md
Code generation codegen_basics.md
Series bindings series_bindings.md
Induced graph induced_graph.md

Project links

Data verified by PyPI on Sep 6, 2026
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Key dates

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  • Released:
Newer release available (14.2.10)

1 maintainer

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Avatar for chriscarrollsmith from gravatar.com chriscarrollsmith

Credits

Author: Teal Insights

GitHub Statistics

Data verified by PyPI on Sep 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT License)

Requires

Python >=3.11

Provides Extra

all fast networkx viz

Tags

dependency excel formula graph spreadsheet transpiler

Classifiers

Report project as malware

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