6.1.2 Plotly Graph Objects

About Graph Objects

The plotly.graph_objects module (typically imported as go) contains an automatically-generated hierarchy of Python classes that represent non-leaf nodes in this figure schema.

Every non-leaf attribute of a figure is represented by an instance of a class in the plotly.graph_objectshierarchy.

Functions in Plotly Express, which is the recommended entry-point into the plotly library, are all built on top of graph objects, and all return instances of plotly.graph_objects.Figure.

Advantage

• Graph objects provide precise data validation. If you provide an invalid property name or an invalid property value as the key to a graph object, an exception will be raised with a helpful error message describing the problem.

• Graph objects contain descriptions of each valid property as Python docstrings, with a full API reference available.

• Properties of graph objects can be accessed using both dictionary-style key lookup (e.g. fig["layout"]) or class-style property access (e.g. fig.layout).

• Graph objects support higher-level convenience functions and "magic underscores" for making updates to already constructed figures

When to use Graph Objects

1. Multiple plots in one figure

What to learn:

• Add traces in figure

• Distinguish scatter plot with "markers", "lines", and "default" mode

• Set the color or line and size of scatters

• Add legend name, axes name

import plotly.graph_objects as go

fig = go.Figure()

# add trace 1
fig.add_trace(go.Scatter(
    x=['a', 'b', 'c', 'd', 'e'], y=[1, 2, 3, 4, 5],
    mode='markers', marker_size= 30,
    name="Positive"
))

fig.add_trace(go.Scatter(
    x=['a', 'b', 'c', 'd', 'e'], y=[3, 3, 3, 3, 3],
    mode='lines',
    name="Neutral",
    line = dict(color='deeppink', width=4, dash='dash')
))

fig.add_trace(go.Scatter(
    x=['a', 'b', 'c', 'd', 'e'], y=[5, 4, 3, 2, 1],
    name="Negative"
))

# Edit the layout
fig.update_layout(title='Multiple plots in one figure',
                   yaxis_title='Value')

fig.show()

2. Multiple plots in multiple figures

What to learn:

• Make subplots

• Deal with categorical data

• Add text on figure

• Change bar mode

Firstly, we create a subplots figure contains 1 row and 2 columns. Then we create a list named "fruit" and pass two different lists of value to it.

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fruit = ['apple','orange','banana']

fig = make_subplots(rows=1, cols=2)
fig.add_trace(
    go.Bar(name = 'Q1', x= fruit, y=[14, 18, 26],  
    text=[14, 18, 26],textposition='auto'),
    row=1, col=1)

fig.add_trace(
    go.Bar(name = 'Q2', x= fruit, y=[30, 50, 40], 
    text= [30, 50, 40],textposition='auto'),
    row=1, col=2)

fig.update_layout(height=500, width=800, title_text="Fruit sales in Q1 and Q2")
fig.show()

Although the chart looks good and clear, it is quite tricky, our audience will easily ignore ticks of Y-axis and have wrong impressions. Therefore, it's better to let the two plots share the Y-axis.

After changing the bar mode and sharing the Y-axis

fig = go.Figure(data=[
    go.Bar(name='Q1', x= fruit, y=[14, 18, 26]),
    go.Bar(name='Q2', x= fruit, y=[30, 50, 40])
])
# Change the bar mode
fig.update_layout(barmode='group', height=500, width=800, 
                    title_text="Fruit sales in Q1 and Q2")
fig.show()

3. Share X-axis Subplots

What to learn:

• Make vertical subplots

• Add subplot titles

fig = make_subplots(rows=3, cols=1, 
                    shared_xaxes=True, 
                    vertical_spacing=0.02,
                   subplot_titles=("Plot 1", "Plot 2", "Plot 3"))

fig.add_trace(go.Scatter(x=[0, 1, 2], y=[10, 11, 12]),
              row=3, col=1)

fig.add_trace(go.Scatter(x=[2, 3, 4], y=[100, 110, 120]),
              row=2, col=1)

fig.add_trace(go.Scatter(x=[3, 4, 5], y=[200, 250, 300]),
              row=1, col=1)

fig.update_layout(height=600, width=600,
                  title_text="Stacked Subplots with Shared X-Axes")
fig.show()

4. Dual-axis plots

Even though some people believe that the dual-axis chart is a great way to easily illustrate the relationship between two different variables and illustrate a lot of information with limited space, I would suggest you not to, unless you had a thoughtful consideration. The Dual-axis chart is hard for most people to intuitively make right statements about two data series. In fact, you can just use two charts to deliver the same information.

Remember? Simple is better than complex.

What to learn:

• Create a dual-axis plot

• Add the main title

• Change the primary and secondary Y-axis title

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Scatter(x=['Jonas', 'Julian', 'Max'], y=[180, 185, 175], name="Height(cm)"),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter(x=['Jonas', 'Julian', 'Max'], y=[90, 85, 75], name="Weight(kg)"),
    secondary_y=True)

# Add figure title
fig.update_layout(title_text="Students Info")

# Set x-axis title
fig.update_xaxes(title_text="Name")

# Set y-axes titles
fig.update_yaxes(title_text="<b>Height</b> ", secondary_y=False)
fig.update_yaxes(title_text="<b>Weight</b>", secondary_y=True)

fig.show()

As we can see, the left Y-axis represents students' height, the right one means weight. It's hard to read the message without the legend.

5. Synchronizing axes in subplots

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

N = 20
x = np.linspace(0, 1, N)

fig = make_subplots(3, 1)
for i in range(1, 4):
    fig.add_trace(go.Scatter(x=x, y=np.random.random(N)), i, 1)
fig.update_xaxes(matches='x')  # use "match" to sync
fig.show()