💡 Problem Formulation: When visualizing data, you may encounter scenarios where multiple plots need to be arranged in a grid format for better comparison and aesthetics. With Python’s Bokeh library, creating grid plots is straightforward. This article covers methods to create grid plots by arranging Bokeh figures in a grid layout, aiming for an output where multiple plots are organized side by side or in a tabular format.
Method 1: Using gridplot()
This method utilizes the Bokeh library’s gridplot() function that lays out individual plot objects in a grid. It provides a high degree of customization, allowing you to specify the number of rows and columns, and how the plots are arranged within the grid.
Here’s an example:
from bokeh.plotting import figure, show from bokeh.layouts import gridplot # Create two figures p1 = figure(width=250, height=250) p2 = figure(width=250, height=250) # Draw some glyphs p1.circle([1, 2, 3], [4, 5, 6]) p2.line([1, 2, 3], [4, 6, 5]) # Create a grid layout grid = gridplot([[p1, p2]]) # Show the layout show(grid)
Output: A grid plot with two plots arranged side by side will be displayed in the browser.
The snippet above creates two simple Bokeh plots and arranges them in a grid using the gridplot() function. The function takes a list of lists as the argument where each sublist represents a row in the grid. In this example, there’s just one row with two plots.
Method 2: Nesting gridplots()
Nesting multiple calls to gridplot() enables complex grid arrangements. This is suitable when creating subgrids within a larger grid plot layout is necessary, which allows for a more modular approach.
Here’s an example:
# Assuming p1, p2 defined as before # Create another two figures p3 = figure(width=250, height=250) p4 = figure(width=250, height=250) # Draw some glyphs on the new figures p3.square([1, 2, 3], [4, 5, 6]) p4.triangle([1, 2, 3], [4, 6, 5]) # Create two separate grid layouts grid1 = gridplot([[p1, p2]]) grid2 = gridplot([[p3, p4]]) # Nest the grids within another gridplot() call nested_grid = gridplot([[grid1],[grid2]]) # Show the nested grid layout show(nested_grid)
Output: A larger grid plot consisting of two grids, each with two plots, stacked vertically will appear in the browser.
The code creates two separate grid plots and then nests them within a larger grid. Each grid plot acts as an element in the larger grid’s structure, allowing complex layouts that maintain a clear visual hierarchy.
Method 3: Using layout()
The layout() function grants additional flexibility over gridplot(). It can handle different sizes and aspect ratios better, making it ideal for complex dashboard-like grid layouts that require dynamic sizing.
Here’s an example:
from bokeh.layouts import layout # Assuming p1, p2, p3, p4 defined as before # Create a layout custom_layout = layout([ [p1, p2], [p3], [p4] ]) # Show the custom layout show(custom_layout)
Output: The browser displays a grid with the first row containing two plots side by side, the second row containing one plot, and the third row also containing one plot.
Here, the layout() function is used to arrange plots with differing aspect ratios, providing more control over the spacing and alignment of plots within the grid. Moreover, it allows the ability to span columns or rows, which may not be possible with gridplot().
Method 4: Using Tabs for Multi-Grid Layouts
For datasets requiring separated visualizations, grouping multiple grid layouts into tabs using Bokeh’s Panel and Tabs widgets can dramatically enhance the usability of your interactive charts.
Here’s an example:
from bokeh.models.widgets import Panel, Tabs # Assuming p1, p2, p3, p4 defined as before # Create two panels for two separate gridplots tab1 = Panel(child=gridplot([[p1, p2]]), title='Scatter Plots') tab2 = Panel(child=gridplot([[p3, p4]]), title='Line Plots') # Arrange panels in Tabs tabs = Tabs(tabs=[tab1, tab2]) # Show the tabs show(tabs)
Output: In the browser, a tabbed interface is generated with one tab displaying a grid of scatter plots and another tab for line plots.
This method is suitable when it’s essential to keep different types of visualizations distinct but accessible within the same interface. It neatly consolidates multiple complex visualizations into a clean, tabbed view.
Bonus One-Liner Method 5: Inline Grid Creation
A quick and straightforward approach for creating simple 2×1 grid plots inline is through a one-liner combining the figure instantiation and glyph methods.
Here’s an example:
show(gridplot([[figure().circle([1,2,3], [4,5,6]), figure().line([1,2,3], [4,6,5])]]))
Output: A concise grid with two plots, one with circles and the other with a line, are displayed side by side.
This compact form leverages Python’s ability to create objects within function calls and is ideal for quick prototyping when complex attributes and customizations are not required.
Summary/Discussion
- Method 1: Using gridplot(). Simple and straightforward. Less flexible for complex layouts.
- Method 2: Nesting gridplots(). Granular control over layout. May introduce complexity for handling nested structures.
- Method 3: Using layout(). Enhanced flexibility. Ideal for dynamic and complex dashboards.
- Method 4: Using Tabs for Multi-Grid Layouts. Excellent for organizing various data visualizations. Requires user to navigate tabs.
- Method 5: Inline Grid Creation. Quick, easy, but limited to simple layouts.