Graphed Excel

Tool to analyze and visualize dependencies between cells in Excel spreadsheets in order to get an understanding of the complexity.

Will generate a graph of the dependencies between cells in an Excel spreadsheet. Data extracted with openpyxl (https://foss.heptapod.net/openpyxl/openpyxl), the graph is generated with the networkx library (https://networkx.org/) and is visualized using matplotlib.

Definitions

Single-cell references in a formula sitting in cell A3 like =A1+A2 is considered a dependency between the node A3 and the nodes A2 and A1.

graph TD
    A3 --> A1
    A3 --> A2
    A3["A3=A1+A2"]

A range defined in a formula like =SUM(B1:B3) is kept as a single node in the graph, but all the containing cells are expanded as dependencies of the range node.

So when a cell, C1 contains =SUM(B1:B3) the graph will look like this:

graph TD
    R -->B1
    R -->B2
    R -->B3
    R["B1:B3"]
    C1 --> R

    C1["C1=SUM(B1:B3)"]

Installation from pypi package

PyPi project: graphedexcel

pip install graphedexcel

Installation from source

python -m venv venv
source venv/bin/activate
pip install -e .

Usage

python -m graphedexcel <path_to_excel_file>

Parameters from --help

usage: graphedexcel [-h] [--as-directed-graph] [--no-visualize]
                    [--layout {spring,circular,kamada_kawai,shell,spectral}]
                    [--config CONFIG] [--output-path OUTPUT_PATH]
                    [--open-image]
                    path_to_excel

Process an Excel file to build and visualize dependency graphs.

positional arguments:
  path_to_excel         Path to the Excel file to process.

options:
  -h, --help            show this help message and exit
  --as-directed-graph, -d
                        Treat the dependency graph as directed.
  --no-visualize, -n    Skip the visualization of the dependency
                        graph.
  --layout, -l {spring,circular,kamada_kawai,shell,spectral}
                        Layout algorithm for graph visualization
                        (default: spring).
  --config, -c CONFIG   Path to the configuration file for
                        visualization. See README for details.
  --output-path, -o OUTPUT_PATH
                        Specify the output path for the generated
                        graph image.
  --open-image          Open the generated image after visualization.
  --hide-legends        Do not show legends in the visualization. (Default: False)

Sample output

The following is the output of running the script on the sample docs/Book1.xlsx file.

===  Dependency Graph Summary ===
Cell/Node count                70
Dependency count              100


===  Most connected nodes     ===
Range Madness!A2:A11           22
Range Madness!B2:B11           11
Range Madness!F1               10
Main Sheet!B5                   4
Main Sheet!B22                  4
Detached !A2:A4                 4
Range Madness!B2                4
Range Madness!B3                4
Range Madness!B4                4
Range Madness!B5                4

===  Most used functions      ===
SUM                             4
POWER                           1

Visualizing the graph of dependencies.
This might take a while...

Graph visualization saved to images/.\Book1.xlsx.png

Sample plot

More in docs/images folder.

Customizing Graph Visualization Settings

You can customize the graph visualization settings by passing a path to a JSON configuration file. This allows you to override the default settings with your own preferences.

Look at https://networkx.org/documentation/stable/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html for the available settings.

Default Settings

The default settings for the graph visualization in the various sizes (from graph_visualizer.py):

# Default settings for the graph visualization
base_graph_settings = {
    "node_size": 50,        # the size of the node
    "width": 0.2,           # the width of the edge between nodes
    "edge_color": "black",  # the color of the edge between nodes
    "linewidths": 0,        # the stroke width of the node border
    "with_labels": False,   # whether to show the node labels
    "font_size": 10,        # the size of the node labels
    "cmap": "tab20b",       # the color map to use for coloring nodes
    "fig_size": (10, 10),   # the size of the figure
}

# Sized-based settings for small, medium, and large graphs
small_graph_settings = {
    "with_labels": False,
    "alpha": 0.8}

medium_graph_settings = {
    "node_size": 30,
    "with_labels": False,
    "alpha": 0.4,
    "fig_size": (20, 20),
}

large_graph_settings = {
    "node_size": 20,
    "with_labels": False,
    "alpha": 0.2,
    "fig_size": (25, 25),
}

Custom JSON Configuration

To override these settings, create a JSON file (e.g., graph_settings.json) with the desired settings. Here is an example of a JSON configuration file:

{
  "node_size": 40,
  "edge_color": "blue",
  "with_labels": true,
  "font_size": 12,
  "alpha": 0.6
}

Using the Custom Configuration

To use the custom configuration, pass the path to the JSON file as an argument to the script:

python -m graphedexcel myexcel.xlsx --config graph_settings.json

This will render the graph using the custom settings defined in the JSON file.

Tests

Just run pytest in the root folder.

pytest

Bandit Security Tests

To run the Bandit (https://github.com/PyCQA/bandit) security tests, you can use the following command. It will report on medium and high severity safety issues.

poetry run bandit -c pyproject.toml -r . -lll

Run with Docker

If you don't want to install the python dependencies on your machine, you can run the script in a Docker container. The following command will build the Docker image and run the script on the sample docs/Book1.xlsx file.

With a powershell terminal:

docker build -t graphedexcel .
docker run --rm -v ${pwd}/docs:/app/docs graphedexcel docs/Book1.xlsx -o docs/av.png

Image will be saved in the docs folder