Python案例如何用Plotly做三维散点图

wen python案例 2026-07-15 163

本文目录导读：

基础三维散点图
带分类的3D散点图
从真实数据创建3D散点图
三维散点图的高级定制
与其他图表结合
主要特性说明

我来为您展示如何使用Plotly创建三维散点图的完整案例。

基础三维散点图

import plotly.graph_objects as go
import numpy as np
# 生成随机数据
np.random.seed(42)
n_points = 500
x = np.random.randn(n_points)
y = np.random.randn(n_points) 
z = np.random.randn(n_points)
# 创建3D散点图
fig = go.Figure(data=[go.Scatter3d(
    x=x, y=y, z=z,
    mode='markers',
    marker=dict(
        size=5,
        color=z,                # 根据z值着色
        colorscale='Viridis',   # 颜色渐变
        opacity=0.8
    )
)])
# 设置布局
fig.update_layout('基础3D散点图',
    scene=dict(
        xaxis_title='X轴',
        yaxis_title='Y轴',
        zaxis_title='Z轴'
    )
)
fig.show()

带分类的3D散点图

import plotly.graph_objects as go
import pandas as pd
import numpy as np
# 生成模拟数据
np.random.seed(42)
n_points = 200
data = {
    'x': np.random.randn(n_points) * 2,
    'y': np.random.randn(n_points) * 2,
    'z': np.random.randn(n_points) * 2,
    'category': np.random.choice(['A', 'B', 'C', 'D'], n_points)
}
df = pd.DataFrame(data)
# 为每个类别创建单独的轨迹
colors = {'A': 'red', 'B': 'blue', 'C': 'green', 'D': 'purple'}
traces = []
for category, color in colors.items():
    category_data = df[df['category'] == category]
    trace = go.Scatter3d(
        x=category_data['x'],
        y=category_data['y'],
        z=category_data['z'],
        mode='markers',
        name=category,
        marker=dict(
            size=6,
            color=color,
            opacity=0.7
        )
    )
    traces.append(trace)
fig = go.Figure(data=traces)
fig.update_layout('带分类的3D散点图',
    scene=dict(
        xaxis_title='X轴',
        yaxis_title='Y轴', 
        zaxis_title='Z轴'
    ),
    legend_title='类别'
)
fig.show()

从真实数据创建3D散点图

import plotly.graph_objects as go
import plotly.express as px
from sklearn.datasets import load_iris
import pandas as pd
# 加载鸢尾花数据集
iris = load_iris()
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['species'] = iris.target_names[iris.target]
# 方法1: 使用plotly.graph_objects
fig1 = go.Figure()
for species_name in df['species'].unique():
    species_data = df[df['species'] == species_name]
    fig1.add_trace(go.Scatter3d(
        x=species_data['sepal length (cm)'],
        y=species_data['sepal width (cm)'],
        z=species_data['petal length (cm)'],
        mode='markers',
        name=species_name,
        marker=dict(
            size=8,
            opacity=0.7
        )
    ))
fig1.update_layout('鸢尾花数据3D散点图',
    scene=dict(
        xaxis_title='花萼长度',
        yaxis_title='花萼宽度',
        zaxis_title='花瓣长度'
    )
)
fig1.show()
# 方法2: 使用plotly.express (更简单)
fig2 = px.scatter_3d(
    df,
    x='sepal length (cm)',
    y='sepal width (cm)',
    z='petal length (cm)',
    color='species','鸢尾花数据3D散点图 (Express)',
    labels={
        'sepal length (cm)': '花萼长度',
        'sepal width (cm)': '花萼宽度',
        'petal length (cm)': '花瓣长度'
    }
)
fig2.show()

三维散点图的高级定制

import plotly.graph_objects as go
import numpy as np
# 生成螺旋数据
def generate_spiral(n_points=300):
    t = np.linspace(0, 4*np.pi, n_points)
    x = np.cos(t) * t
    y = np.sin(t) * t
    z = t / np.pi
    color = t / (4*np.pi)  # 用于颜色映射
    return x, y, z, color
x, y, z, colors = generate_spiral()
# 创建高级定制3D散点图
fig = go.Figure(data=[go.Scatter3d(
    x=x,
    y=y,
    z=z,
    mode='markers',
    marker=dict(
        size=8,              # 点的大小
        color=colors,        # 颜色值
        colorscale='Rainbow', # 颜色方案
        opacity=0.8,         # 透明度
        symbol='circle',     # 点的形状
        line=dict(
            width=2,
            color='white'    # 点的边框
        ),
        colorbar=dict(
            title='位置',
            titleside='right'
        ),
        showscale=True
    ),
    text=[f'点{i}: ({x[i]:.2f}, {y[i]:.2f}, {z[i]:.2f})' 
          for i in range(len(x))],  # 悬停文本
    hoverinfo='text'
)])
# 自定义布局
fig.update_layout({
        'text': '3D螺旋散点图 (高级定制)',
        'y': 0.95,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    scene=dict(
        xaxis=dict(
            title='X轴',
            gridcolor='lightgray',
            showbackground=True,
            backgroundcolor='black'
        ),
        yaxis=dict(
            title='Y轴',
            gridcolor='lightgray',
            showbackground=True,
            backgroundcolor='black'
        ),
        zaxis=dict(
            title='Z轴',
            gridcolor='lightgray',
            showbackground=True,
            backgroundcolor='black'
        ),
        camera=dict(
            eye=dict(x=1.5, y=1.5, z=0.5)  # 视角位置
        ),
        aspectmode='cube'  # 保持比例
    ),
    paper_bgcolor='white',
    height=600,
    width=800,
    showlegend=False
)
# 添加动画效果
fig.update_layout(
    updatemenus=[{
        'type': 'buttons',
        'showactive': False,
        'buttons': [{
            'label': '自动旋转',
            'method': 'animate',
            'args': [None, {
                'frame': {'duration': 50, 'redraw': True},
                'fromcurrent': True,
                'mode': 'immediate'
            }]
        }]
    }]
)
fig.show()

与其他图表结合

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
# 创建子图：3D散点图 + 2D投影
np.random.seed(42)
n_points = 100
x = np.random.randn(n_points) * 2
y = np.random.randn(n_points) * 2
z = np.random.randn(n_points) * 2
colors = z  # 使用z值作为颜色
fig = make_subplots(
    rows=2, cols=2,
    specs=[
        [{'type': 'scatter3d', 'rowspan': 2}, {'type': 'scatter'}],
        [None, {'type': 'scatter'}]
    ],
    subplot_titles=('3D视图', 'X-Y投影', '', 'X-Z投影')
)
# 3D散点图
fig.add_trace(
    go.Scatter3d(
        x=x, y=y, z=z,
        mode='markers',
        marker=dict(size=5, color=colors, colorscale='Viridis'),
        showlegend=False
    ),
    row=1, col=1
)
# X-Y投影
fig.add_trace(
    go.Scatter(
        x=x, y=y,
        mode='markers',
        marker=dict(size=8, color=colors, colorscale='Viridis', 
                    showscale=False),
        showlegend=False
    ),
    row=1, col=2
)
# X-Z投影
fig.add_trace(
    go.Scatter(
        x=x, y=z,
        mode='markers',
        marker=dict(size=8, color=colors, colorscale='Viridis',
                    showscale=False),
        showlegend=False
    ),
    row=2, col=2
)
fig.update_layout('3D散点图及其2D投影',
    height=600,
    width=900
)
fig.show()