How can I align split violin plots with overlaid box plots in Seaborn?

Below is an explanation and a couple of potential solutions.

What’s Happening

When you set split=True, Seaborn divides each category’s violin into two halves (one for each hue level) by internally adjusting the width and positions. The violin positioning uses a “native width” (computed from your categorical or numeric x values) and then applies offsets to show the split. Box plots, however, rely on the dodge parameter (and a different positioning logic) to align with the corresponding category values. Because these two positioning mechanisms are not exactly the same, you see a misalignment when overlaying box plots on split violins.

How to Fix It

A couple of options can help remedy the alignment issue:

1. Enable Native Scaling for the Violins
   When plotting violin plots that use split violins, you can set the parameter
   native_scale=True. This makes the violins respect the native width of your x variables. For example:

   PYTHONCopied
   sns.violinplot(
       data=data.melt(id_vars="column"),
       x="column",
       y="value",
       hue="variable",
       split=True,
       palette="Paired",
       bw_method="silverman",
       inner=None,
       native_scale=True  # ensures the violins use the native x-axis spacing
   )

2. Ensure Consistent Dodge/Width Settings
   When overlaying, make sure the dodge settings and widths used for the box plots match those of the violins. Since the violin internally computes its positioning (without any explicit dodge when split=True) you may need to manually adjust the dodge offset in the box plot call so it “fits” over the split violins. For example, you can try:

   PYTHONCopied
   sns.boxplot(
       data=data.melt(id_vars="column"),
       x="column",
       y="value",
       hue="variable",
       palette="Paired",
       width=0.3,
       flierprops={"marker": "o", "markersize": 3},
       dodge=True  # this dodge should match how the violins offset hue values
   )

   If the box plots are still misaligned, you may consider a manual shift by modifying the position values (using the positions provided by the categorical encoder) so that they overlay exactly on top of the violins.

Putting It All Together

Here’s a revised version of your second example (with both violin and box plots) that attempts to align the two:

PYTHONCopied
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns

sns.set_theme()

data1 = np.random.normal(0, 1, 1000)
data2 = np.random.normal(1, 2, 1000)

data = pd.concat(
    [
        pd.DataFrame({"column": "1", "data1": data1, "data2": data2}),
        pd.DataFrame({"column": "2", "data3": data2, "data4": data1}),
    ],
    axis="rows",
)

# Melt the data for easier plotting
melted = data.melt(id_vars="column")

def mkplot_aligned():
    fig, ax = plt.subplots()

    # Draw the split violin plot with native_scale enabled
    sns.violinplot(
        data=melted,
        x="column",
        y="value",
        hue="variable",
        split=True,
        palette="Paired",
        bw_method="silverman",
        inner=None,
        native_scale=True,  # use native scaling to help with alignment
        ax=ax,
    )

    # Overlay the box plots with consistent dodge and width parameters
    sns.boxplot(
        data=melted,
        x="column",
        y="value",
        hue="variable",
        palette="Paired",
        width=0.3,
        flierprops={"marker": "o", "markersize": 3},
        dodge=True,
        ax=ax,
        legend=False,
    )

    plt.show()

mkplot_aligned()

Final Notes

Because the violin plot’s split behavior and the box plot's dodge positioning come from two separate internal routines, there may be scenarios where manual adjustment is needed. If the above does not perfectly align the box plots on your system (or if you require further fine-tuning), consider calculating the positional offsets (or explore using the same dodge value for both) to force a manual alignment.

This should help address the misalignment issue you’re experiencing.