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:
- Build a dependency graph from an Excel workbook (
excel_grapher.grapher). - Evaluate formulas with Excel semantics over that graph (
excel_grapher.evaluator.FormulaEvaluator). - Export standalone Python (
excel_grapher.exporter.CodeGenerator) and/or a new.xlsxfrom the sameGraphReadView(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
from excel_grapher.series_bindings import load_series_bindings
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 a standalone Python package (requires series bindings)
bindings = load_series_bindings(workbook_path.with_suffix(".bindings.yaml"))
with CodeGenerator(graph) as gen:
modules = gen.generate_modules(
series_bindings=bindings,
bindings_workbook=workbook_path,
)
# 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 — inverted-tree compute_* functions over named tensors and scalars (as_records for a Records
view). 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 |
| Graph evaluation | codegen_basics.md |
| Series bindings | series_bindings.md |
| Induced graph | induced_graph.md |
1 maintainer
Release files for excel-grapher 20.9.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| excel_grapher-20.9.1.tar.gz | 6.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| excel_grapher-20.9.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.7 MB
Release files / excel_grapher-20.9.1.tar.gz
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| Tags | Source |
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| Uploaded via |
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