如何移除hvplot网格热力图左侧色条并对齐共享坐标轴？

Absolutely, hvplot fully supports both hiding the extra left colorbar and aligning shared axes for your heatmap subplots! Let's walk through the adjustments you need to make to your code:

Step 1: Use Shared Colorbar to Eliminate Redundant Colorbars #

By default, each subplot created with col='group' will render its own colorbar. To fix this, use the shared_colorbar parameter to share a single colorbar across all subplots, and you can specify its position to avoid the left-side colorbar.

Step 2: Force Axis Alignment #

Ensure your subplots share both x and y axes to match the alignment in the reference documentation using the shared_axes parameter.

Modified Code #

import pandas as pd
import hvplot.pandas
from bokeh.sampledata.unemployment1948 import data

# Your original data preprocessing
data.Year = data.Year.astype(str)
data = data.set_index('Year')
data.drop('Annual', axis=1, inplace=True)
data.columns.name = 'Month'
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()
df = df.tail(40)
df['group'] = [1,2]*20

# Updated plot with desired settings
df.hvplot.heatmap(
    x='Year', 
    y='Month', 
    C='rate', 
    col='group', 
    shared_colorbar=True,  # Share one colorbar across all subplots
    colorbar_position='right',  # Place colorbar on the right (hides left ones)
    shared_axes=True,  # Align x/y axes perfectly across subplots
    yaxis='left'  # Keep y-axis consistent on the left for alignment
)

Parameter Explanations #

• shared_colorbar=True: This replaces individual colorbars per subplot with a single shared one, eliminating the redundant left colorbar. You can adjust its position with colorbar_position (options: 'right', 'left', 'top', 'bottom').
• shared_axes=True: Forces all subplots to use identical x and y axis ranges, ticks, and labels, ensuring perfect alignment just like the reference example.
• yaxis='left': Ensures the y-axis is displayed on the left for all subplots, adding to the consistent aligned look.

Alternative: Fine-Grained Colorbar Control #

If you need more control (e.g., hiding a specific subplot's colorbar manually), you can use hvplot's opts method:

# Create the initial plot with colorbars enabled for all subplots
plot = df.hvplot.heatmap(x='Year', y='Month', C='rate', col='group', colorbar=True)

# Hide colorbar for the first subplot (index 0) and keep it for the second
plot = plot.opts(
    hv.opts.HeatMap(subplot_index=0, colorbar=False),
    hv.opts.HeatMap(subplot_index=1, colorbar=True)
)

plot

Either approach will give you the clean, aligned heatmap subplots with a single colorbar that you're looking for!

内容的提问来源于stack exchange，提问作者Lundi Lin