.violin()

In Plotly, the .violin() method generates a violin plot. This plot uses kernel density estimation (KDE) to display the distribution of numeric data, providing a detailed view of data density across different values.

Syntax

plotly.express.violin(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, ...)

• data_frame: It is the input data as a Pandas DataFrame.
• x: It specifies the column name for the x-axis categories.
• y: It specifies the column name for the y-axis numeric data.
• color: It specifies the column name for color-coding categories.
• facet_row: It specifies the column name to create row-wise subplots.
• facet_col: It specifies the column name to create column-wise subplots.

Note: The ellipsis (…) indicates that there can be additional optional parameters beyond those listed here.

Example

Here is an example of creating a violin plot using the .violin() method:

# Importing necessary libraries
import plotly.express as px  # Importing 'plotly.express' for data visualization
import pandas as pd  # Importing 'pandas' for data manipulation and analysis

# Creating a sample dataset by defining a dictionary with two keys: 'Category' and 'Value'
data = {'Category': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
        'Value': [1, 2, 3, 4, 5, 6, 7, 8, 9]}

# Converting the dictionary into a Pandas DataFrame
df = pd.DataFrame(data)

# Using Plotly Express to create a violin plot with 'Category' on the x-axis and 'Value' on the y-axis
fig = px.violin(df, x="Category", y="Value")

# Displaying the plot
fig.show()

The above code produces the following output: