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.

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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:

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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> [--verbose] [--no-visualize] [--as-directed-graph] [--open-image]

Depending on the size of the spreadsheet you might want to adjust the plot configuration in the code to to make the graph more readable (remove labels, decrease widths and sizes etc) - you can find the configuration in graph_visualizer.py with settings for small, medium and large graphs. You can adjust the configuration to your needs - but this only working if you run from source.

Arguments

--verbose will dump formula cell contents during (more noisy)

--no-visualize will skip the visualization step and only print the summary (faster)

--as-directed-graph will keep the direction of the graph as it is in the excel file and it will be visualized with arrows, otherwise it will be simplified to an undirected graph (slower)

--open-image will open the generated image in the default image viewer (only on Windows)

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 <path_to_excel_file> --config <path to grap_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

Contribute

Feel free to contribute by opening an issue or a pull request.

You can help with the following, that I have thought of so far:

• Add more tests
• Improve the code
• Add more features
• Improve the visualization and the ease of configuration
• Add more examples
• Add more documentation