Chart layouts

In this lesson, we will learn about chart layouts in Plotly. Layouts can make your plots more presentable and meaningful. We will explore different layout properties and see how we can customize the chart layout to our preferences.

Importing Libraries

For this lesson, we will need the following libraries:

import plotly.express as px
import plotly.graph_objects as go

Basic Chart Layout

Let's start by creating a simple scatter plot using the Plotly Express library and the built-in "iris" dataset.

import plotly.express as px

# Load the iris dataset
data = px.data.iris()

# Create a scatter plot
fig = px.scatter(data, x='sepal_width', y='sepal_length', color='species')

# Show the plot
fig.show()

Now, let's customize the chart layout using the layout property of the graph_objects library.

import plotly.graph_objects as go

# Load the iris dataset
data = px.data.iris()

# Create a scatter plot
fig = px.scatter(data, x='sepal_width', y='sepal_length', color='species')

# Customize the layout
fig.update_layout(
    title="Sepal Width vs Sepal Length",
    xaxis_title="Sepal Width",
    yaxis_title="Sepal Length",
    legend_title="Species",
    font=dict(
        family="Courier New, monospace",
        size=14,
        color="black"
    )
)

# Show the plot
fig.show()

In the example above, we used the update_layout method to customize the chart layout. We updated the chart title, x-axis title, y-axis title, legend title, and the font.

Customizing Axis Properties

You can further customize the axis properties, such as axis range, tick format, and grid lines.

import plotly.graph_objects as go

# Load the iris dataset
data = px.data.iris()

# Create a scatter plot
fig = px.scatter(data, x='sepal_width', y='sepal_length', color='species')

# Customize the layout
fig.update_layout(
    title="Sepal Width vs Sepal Length",
    xaxis=dict(
        title="Sepal Width",
        range=[2, 4.5],
        tickmode="linear",
        tick0=2,
        dtick=0.2,
        gridcolor="lightgray",
    ),
    yaxis=dict(
        title="Sepal Length",
        range=[4, 8],
        tickmode="linear",
        tick0=4,
        dtick=0.5,
        gridcolor="lightgray",
    ),
    legend_title="Species",
    font=dict(
        family="Courier New, monospace",
        size=14,
        color="black"
    )
)

# Show the plot
fig.show()

In the example above, we used the xaxis and yaxis properties to customize the x-axis and y-axis, respectively. We set the axis range, tick format, and grid lines.

Customizing the Legend

You can also customize the legend properties, such as the legend position, orientation, and background color.

import plotly.graph_objects as go

# Load the iris dataset
data = px.data.iris()

# Create a scatter plot
fig = px.scatter(data, x='sepal_width', y='sepal_length', color='species')

# Customize the layout
fig.update_layout(
    title="Sepal Width vs Sepal Length",
    xaxis_title="Sepal Width",
    yaxis_title="Sepal Length",
    legend=dict(
        title="Species",
        x=0,
        y=1,
        bgcolor="rgba(255, 255, 255, 0.5)",
        bordercolor="black",
        borderwidth=1,
        orientation="h"
    ),
    font=dict(
        family="Courier New, monospace",
        size=14,
        color="black"
    )
)

# Show the plot
fig.show()

In the example above, we used the legend property to customize the legend. We set the legend position, orientation, and background color.

Conclusion

In this lesson, we learned how to customize chart layouts in Plotly. We explored different layout properties and saw how we can make our plots more presentable and meaningful. Now, you can create more visually appealing and informative charts using Plotly's layout customization options.