如何在子图中将具有值和 x-y 位置的列绘制为彩色网格

• 关键是要用熊猫重塑每组数据。DataFrame.pivot 转换为 seaborn.heatmap 的正确格式。'z'
  • 定义 和 以及整个数据集的 and： 和vminvmaxminmaxvmin=df.I.min()vmax=df.I.max()
• 以下代码使用 预定义了 和 all。figaxesplt.subplots
  • 如何在多个子图中绘图显示了创建子图的其他方法，包括这个答案，用 创建图形，用 添加子图。fig = plt.figure()fig.add_subplot(2, 3, idx)
• 引用：
  • 子图中海生热图的一个颜色条
  • 在绘图顶部移动热图颜色条
• 在 python v3.12.0、pandas v2.1.2、matplotlib v3.8.1、seaborn v0.13.0 中测试。

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# sample data
df = pd.DataFrame(list(itertools.product(range(8), range(8), range(6))), 
                  columns=['x', 'y', 'z'])
np.random.seed(20231116)  # for reproducible data
df['I'] = np.random.rand(df.shape[0])

# create the figure and axes
fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)

# flatten the axes into a 1d array for easy access
axes = axes.flat

# add a separate axes for the colorbar
cbar_ax = fig.add_axes([0.3, .03, .4, .03])

# enumerate is specifically for adding the colorbar
# zip each group of 'z' data to the appropriate axes
for i, (ax, (z, data)) in enumerate(zip(axes, df.groupby('z'))):

    # pivot data into the correct shape for heatmap
    data = data.pivot(index='y', columns='x', values='I')

    # plot the heatmap
    sns.heatmap(data=data, cmap='viridis', ax=ax, cbar=i == 0, vmin=df.I.min(), vmax=df.I.max(),
                cbar_ax=None if i else cbar_ax, cbar_kws=dict(location="bottom"))

    # add a title
    ax.set(title=f'Z: {z}')

    # invert the yaxis to match the OP
    ax.invert_yaxis()

data为z: 5

x         0         1         2         3         4         5         6         7
y                                                                                
0  0.488408  0.855913  0.339374  0.452842  0.510380  0.690491  0.448773  0.500916
1  0.273653  0.561840  0.860269  0.387470  0.170281  0.718488  0.256749  0.463527
2  0.546085  0.093934  0.273339  0.503968  0.063212  0.537974  0.867814  0.135719
3  0.071505  0.792265  0.919784  0.559663  0.733996  0.032003  0.475792  0.690789
4  0.474310  0.265576  0.841875  0.496676  0.603356  0.328808  0.039460  0.461778
5  0.439142  0.119253  0.842653  0.155213  0.798092  0.093709  0.899745  0.927067
6  0.548373  0.259983  0.295939  0.700694  0.040197  0.679880  0.153048  0.328768
7  0.216977  0.176777  0.238436  0.610802  0.705161  0.614877  0.813430  0.527120

• 使用 和 实现，而不是plt.figurefig.add_subplotplt.subplots

# create the figure and axes
fig = plt.figure(figsize=(15, 10))

# add a separate axes for the colorbar
cbar_ax = fig.add_axes([0.3, .03, .4, .03])

# enumerate is specifically for adding the colorbar and adding an axes
for i, (z, data) in enumerate(df.groupby('z')):

    # pivot data into the correct shape for heatmap
    data = data.pivot(index='y', columns='x', values='I')

    # create the axes
    ax = fig.add_subplot(2, 3, i+1)

    # plot the heatmap
    sns.heatmap(data=data, cmap='viridis', ax=ax, cbar=i == 0, vmin=df.I.min(), vmax=df.I.max(),
                cbar_ax=None if i else cbar_ax, cbar_kws=dict(location="bottom"))

    # add a title
    ax.set(title=f'Z: {z}')

    # invert the yaxis to match the OP
    ax.invert_yaxis()