Splitting Layout and Plot Coding in Python Projects

We often work on Python projects involving generating plots or graphs using libraries like Matplotlib or Plotly. As our projects grow, organizing code by separating layout design from plotting logic becomes essential. How can we achieve this neatly? Let’s explore a method to split these concerns across different files for better project structure.

What You’ll Learn

Discover an effective way to organize your Python code by dividing layout specifications and plot generation into separate files. This approach enhances code readability, making it easier to maintain and scale your projects.

Introduction to Problem and Solution

Organizing a project efficiently involves identifying segments of code that can be decoupled without losing functionality. For visualization tasks, separating visual appearance (layout) from data processing and plotting logic allows for reusing layouts or adapting them with minimal changes to the underlying plotting commands.

To achieve this separation, we create two distinct files: one for defining layouts (layout.py) and another for handling the plotting logic (plot.py). The layout.py file contains definitions related to plot appearance – such as size, color schemes, titles, etc., while plot.py focuses on data manipulation and rendering visuals based on layout specifications.



# Define a basic layout
def basic_layout():
    return {
        'title': 'My Awesome Plot',
        'xaxis_title': 'X-Axis Label',
        'yaxis_title': 'Y-Axis Label',
        # Add more properties here as needed

# Copyright PHD


import matplotlib.pyplot as plt
from layout import basic_layout  # Importing our defined layout

def generate_plot(data):
    fig, ax = plt.subplots()

    # Applying layout settings
    layout = basic_layout()


# Example usage:
generate_plot([1, 2, 3, 4])

# Copyright PHD


By separating concerns – layout specifics in layout.py and plotting functions in plot.py, modularity is enhanced. Defining visual aspects in layout.py keeps these details independent from data handling or business logic found in plot.py. This separation simplifies adjustments and enables applying a consistent look across the application with minimal duplication of effort.

Using imports (from layout import basic_layout), integrating these separated pieces when generating plots is seamless. This structure supports scalability; maintaining clear boundaries between visual definitions and their implementation keeps modifications localized and manageable as complexity grows.

  1. How do I install Matplotlib?

  2. To install Matplotlib, you can use the following command:

  3. pip install matplotlib
  4. # Copyright PHD
  5. Can I use this approach with other plotting libraries like Plotly?

  6. Yes! This concept is library-agnostic. Adjustments may be required based on how specific libraries handle layouts versus plotting commands.

  7. Is it possible to define multiple layouts in layout.py?

  8. Absolutely! Add more functions or dictionaries representing unique styles you want. Import them into your plotting scripts as needed.

  9. How can I share common styles across different layouts?

  10. Consider using base dictionaries for shared styles which individual layouts can copy/update according their needs via .update() method or similar techniques.

  11. Can I include interactive elements using this method?

  12. Yes. While interaction often requires additional callbacks/handlers within plot.py, static elements related to interaction could still reside within layout.


Adopting a systematic approach towards separating presentation details from business/logic processes serves aesthetic purposes and aids maintainability at scale´┐Ża principle applicable well beyond just visualization tasks within software engineering domains.

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