如何基于DataFrame用Matplotlib绘制以j、k为索引的3D柱状图？

一、Matplotlib 优化实现 #

Matplotlib默认的3D柱状图确实容易显得杂乱，通过调整视角、柱体尺寸、配色和刻度等细节能大幅提升美观度：

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

# 加载数据
data = pd.DataFrame({
    'j': [16,19,21,24,25,25,26,26,26,26,27,27,27,27],
    'k': [36,40,52,41,37,45,31,36,42,47,30,35,36,38],
    'z': [3.34541e-07,4.4038e-07,1.24715e-06,9.13244e-07,6.33979e-07,5.89413e-07,7.83958e-07,6.24651e-07,5.44847e-07,4.77851e-07,8.50074e-07,5.51727e-07,1.2272e-06,5.77199e-07]
})

# 将非连续的j、k映射为连续坐标轴索引，避免柱体分散重叠
j_unique = np.unique(data['j'])
k_unique = np.unique(data['k'])
x = np.array([np.where(j_unique == val)[0][0] for val in data['j']])
y = np.array([np.where(k_unique == val)[0][0] for val in data['k']])
z = data['z'].values

# 设置柱体宽度和深度，避免互相遮挡
dx = dy = 0.6

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111, projection='3d')

# 用颜色映射区分柱体高度，增强可读性
colors = plt.cm.viridis(z / z.max())
ax.bar3d(x, y, np.zeros_like(z), dx, dy, z, color=colors)

# 配置坐标轴标签与刻度，匹配原始j、k值
ax.set_xlabel('j')
ax.set_xticks(np.arange(len(j_unique)))
ax.set_xticklabels(j_unique)
ax.set_ylabel('k')
ax.set_yticks(np.arange(len(k_unique)))
ax.set_yticklabels(k_unique)
ax.set_zlabel('z')

# 调整视角，找到最清晰的展示角度
ax.view_init(elev=30, azim=-60)

# 隐藏多余网格和背景，让图表更简洁
ax.grid(False)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False

plt.tight_layout()
plt.show()

二、Plotly 优化实现 #

Plotly的3D柱状图默认布局需要调整，通过自定义布局、hover信息和视角来优化展示效果：

import plotly.graph_objects as go
import pandas as pd

# 加载数据
data = pd.DataFrame({
    'j': [16,19,21,24,25,25,26,26,26,26,27,27,27,27],
    'k': [36,40,52,41,37,45,31,36,42,47,30,35,36,38],
    'z': [3.34541e-07,4.4038e-07,1.24715e-06,9.13244e-07,6.33979e-07,5.89413e-07,7.83958e-07,6.24651e-07,5.44847e-07,4.77851e-07,8.50074e-07,5.51727e-07,1.2272e-06,5.77199e-07]
})

fig = go.Figure(data=[go.Bar3d(
    x=data['j'],
    y=data['k'],
    z=data['z'],
    width=0.8,
    depth=0.8,
    marker=dict(
        color=data['z'],
        colorscale='viridis',
        colorbar=dict(title='z值')
    ),
    # 自定义hover信息，方便查看完整数据
    text=[f'j:{j}<br>k:{k}<br>z:{z:.2e}' for j,k,z in zip(data['j'], data['k'], data['z'])],
    hoverinfo='text'
)])

fig.update_layout(
    scene=dict(
        xaxis_title='j',
        yaxis_title='k',
        zaxis_title='z',
        # 调整视角位置，确保所有柱体清晰可见
        camera=dict(eye=dict(x=1.5, y=-1.5, z=0.8)),
        xaxis=dict(showgrid=False),
        yaxis=dict(showgrid=False),
        zaxis=dict(showgrid=False)
    ),
    width=800,
    height=600,
    margin=dict(l=0, r=0, b=0, t=0)
)

fig.show()

内容的提问来源于stack exchange，提问作者Alberto Sciuto