π‘ Problem Formulation: Visualizing multiple datasets can be challenging, especially when trying to compare proportions across different categories. Pie charts are a powerful tool for such comparisons, but when multiple pie charts are necessary, arranging them as subplots with custom sizes often leads to confusion. The desired output is to present a clear arrangement of multiple pie charts of specified sizes within a single figure using Python’s Plotly library.
Method 1: Using Plotly Subplots Function
The make_subplots
function from Plotly’s plotly.subplots
module allows for the creation of a figure with a grid layout onto which pie charts can be added as subplots. Each subplot can be customized in size by adjusting the grid properties within the function.
Here’s an example:
import plotly.graph_objs as go from plotly.subplots import make_subplots fig = make_subplots(rows=1, cols=2, specs=[[{'type':'pie'}, {'type':'pie'}]]) fig.add_trace(go.Pie(labels=['A', 'B', 'C'], values=[10, 15, 75]), row=1, col=1) fig.add_trace(go.Pie(labels=['X', 'Y', 'Z'], values=[35, 40, 25]), row=1, col=2) fig.update_layout(width=800, height=400) fig.show()
Output: A figure containing two pie charts side by side.
This code uses Plotly to create a figure with two columns destined for pie charts. By using the add_trace()
method twice, we put each pie chart into its respective subplot position. Lastly, update_layout()
allows adjusting the overall figure dimensions.
Method 2: Customizing Figure Size with Update_traces
With Plotly’s update_traces
method, customizing different aspects of individual pie charts is possible. It can adjust properties directly related to the pie charts, allowing for better control over their appearance within the subplot grid.
Here’s an example:
fig = make_subplots(rows=1, cols=2, subplot_titles=('First', 'Second'), specs=[[{'type':'pie'}, {'type':'pie'}]]) fig.add_trace(go.Pie(labels=['A', 'B', 'C'], values=[10, 15, 75]), row=1, col=1) fig.add_trace(go.Pie(labels=['X', 'Y', 'Z'], values=[35, 40, 25]), row=1, col=2) fig.update_traces(textinfo='percent+label', pull=[0, 0.1, 0]) fig.update_layout(width=800, height=500) fig.show()
Output: A figure with two distinctly styled pie charts.
This uses update_traces()
to change the display of text information and the outward ‘pull’ of chart segments for emphasis. The figure layout is adjusted to set the figure’s size.
Method 3: Adjusting Domain for Each Pie Chart
Instead of using traditional subplots, the domain for each pie chart trace can be manually specified. This allows for the size and position of each pie to be directly controlled.
Here’s an example:
fig = go.Figure() fig.add_trace(go.Pie(labels=['A', 'B', 'C'], values=[10, 15, 75], domain=dict(x=[0, 0.5], y=[0, 0.5]))) fig.add_trace(go.Pie(labels=['X', 'Y', 'Z'], values=[35, 40, 25], domain=dict(x=[0.5, 1], y=[0, 0.5]))) fig.update_layout(width=700, height=350) fig.show()
Output: A figure with two pie charts, each filling a different area of the plot.
This code individually assigns a coordinate space (domain) for each pie chart within the figure canvas, effectively functioning like subplots. The size of the figure is then adjusted through the update_layout()
function.
Method 4: Using Figure Factory Module
The create_pie()
method within Plotly’s figure_factory
module can generate a figure containing multiple pie charts, allowing direct control over each chart’s size.
Here’s an example:
import plotly.figure_factory as ff pies_data = [ {'labels': ['A', 'B', 'C'], 'values': [10, 15, 75], 'domain': {'x': [0, 0.5], 'y': [0, 0.5]}}, {'labels': ['X', 'Y', 'Z'], 'values': [35, 40, 25], 'domain': {'x': [0.5, 1], 'y': [0, 0.5]}} ] fig = ff.create_pie(pies_data) fig.update_layout(width=700, height=350) fig.show()
Output: A bespoke figure containing two pie charts with defined sizes.
Here we pass a list of dictionaries to the create_pie
method, each representing a pie chart data set with an associated domain for its position and size. Then the figure size is finalized with update_layout()
.
Bonus One-Liner Method 5: Using a Python List Comprehension
The efficiency of Python’s list comprehension can be utilized to generate multiple pie chart subplots in one line of code.
Here’s an example:
fig = make_subplots(rows=2, cols=2, specs=[[{'type':'pie'}, {'type':'pie'}], [{'type':'pie'}, {'type':'pie'}]]) [fig.add_trace(go.Pie(labels=['A', 'B', 'C'], values=[i*10, i*15, i*75]), row=i//2+1, col=i%2+1) for i in range(4)] fig.update_layout(width=800, height=800) fig.show()
Output: A figure displaying four pie charts in a grid layout.
A list-comprehension shorthand drops the pie chart trace addition logic into a condensed line, iterating over a range to define each pie’s position in the grid. Custom sizes are still manually adjustable with update_layout()
.
Summary/Discussion
- Method 1: Using Plotly Subplots Function. Offers a standardized way to create subplots. It could be less flexible for complex layouts.
- Method 2: Customizing Figure Size with Update_traces. Convenient for styling individual traces. Can get verbose with multiple customizations.
- Method 3: Adjusting Domain for Each Pie Chart. High degree of control over chart placement and size. Potentially tedious for many charts.
- Method 4: Using Figure Factory Module. Simplifies the creation of multiple charts with diverse domains. Slightly less intuitive due to the need for a specific data structure.
- Bonus One-Liner Method 5: Using a Python List Comprehension. Great for quickly replicating similar subplots, but might obfuscate readability for newcomers.