Plotly Python数据可视化教程：40+交互式图表创建与API选择指南

plotly by davila7/claude-code-templates

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安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill plotly

Python Web框架数据可视化数据分析

🇨🇳中文介绍

Plotly

用于创建交互式、出版质量可视化的 Python 绘图库，支持 40 多种图表类型。

快速开始

安装 Plotly：

uv pip install plotly

使用 Plotly Express（高级 API）的基本用法：

import plotly.express as px
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 3, 4],
    'y': [10, 11, 12, 13]
})

fig = px.scatter(df, x='x', y='y', title='My First Plot')
fig.show()

API 选择指南

使用 Plotly Express (px)

适用于快速创建具有合理默认设置的标准可视化：

处理 pandas DataFrames
创建常见图表类型（散点图、折线图、条形图、直方图等）
需要自动颜色编码和图例
希望代码量最小（1-5 行）

完整指南请参阅 reference/plotly-express.md。

使用 Graph Objects (go)

适用于精细控制和自定义可视化：

Plotly Express 中不包含的图表类型（3D 网格、等值面、复杂金融图表）
从零开始构建复杂的多轨迹图形
需要对单个组件进行精确控制
创建具有自定义形状和注释的专用可视化

完整指南请参阅 reference/graph-objects.md。

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注意： Plotly Express 返回的是 graph objects 的 Figure 对象，因此可以结合两种方法使用：

fig = px.scatter(df, x='x', y='y')
fig.update_layout(title='Custom Title')  # 在 px 图形上使用 go 方法
fig.add_hline(y=10)                     # 添加形状

Plotly 支持 40 多种图表类型，按类别组织：

基础图表： 散点图、折线图、条形图、饼图、面积图、气泡图

统计图表： 直方图、箱线图、小提琴图、分布图、误差条

科学图表： 热力图、等高线图、三元图、图像显示

金融图表： K线图、OHLC 图、瀑布图、漏斗图、时间序列图

地图： 散点地图、等值区域图、密度地图（地理可视化）

3D 图表： 3D 散点图、曲面图、网格图、锥形图、体积图

专业图表： 旭日图、树状图、桑基图、平行坐标图、仪表图

所有图表类型的详细示例和用法，请参阅 reference/chart-types.md。

子图： 创建具有共享轴的多图图形：

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))
fig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)

模板： 应用协调一致的样式：

fig = px.scatter(df, x='x', y='y', template='plotly_dark')
# 内置模板：plotly_white, plotly_dark, ggplot2, seaborn, simple_white

自定义： 控制外观的每个方面：

颜色（离散序列、连续色阶）
字体和文本
坐标轴（范围、刻度、网格）
图例
边距和尺寸
注释和形状

完整的布局和样式选项，请参阅 reference/layouts-styling.md。

内置交互功能：

可自定义数据的悬停提示框
平移和缩放
图例切换
框选/套索选择
时间序列的范围滑块
按钮和下拉菜单
动画

自定义悬停模板

fig.update_traces( hovertemplate='%{x} Value: %{y:.2f}<extra></extra>' )

添加范围滑块

fig.update_xaxes(rangeslider_visible=True)

动画

fig = px.scatter(df, x='x', y='y', animation_frame='year')

完整的交互性指南，请参阅 reference/export-interactivity.md。

交互式 HTML：

fig.write_html('chart.html')                       # 完整独立文件
fig.write_html('chart.html', include_plotlyjs='cdn')  # 较小文件

静态图像（需要 kaleido）：

uv pip install kaleido



fig.write_image('chart.png')   # PNG
fig.write_image('chart.pdf')   # PDF
fig.write_image('chart.svg')   # SVG

完整的导出选项，请参阅 reference/export-interactivity.md。

科学数据可视化

import plotly.express as px

# 带趋势线的散点图
fig = px.scatter(df, x='temperature', y='yield', trendline='ols')

# 矩阵热力图
fig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')

# 3D 曲面图
import plotly.graph_objects as go
fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])

# 分布比较
fig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)

# 显示所有点的箱线图
fig = px.box(df, x='category', y='value', points='all')

# 小提琴图
fig = px.violin(df, x='group', y='measurement', box=True)

时间序列与金融

# 带范围滑块的时间序列
fig = px.line(df, x='date', y='price')
fig.update_xaxes(rangeslider_visible=True)

# K线图
import plotly.graph_objects as go
fig = go.Figure(data=[go.Candlestick(
    x=df['date'],
    open=df['open'],
    high=df['high'],
    low=df['low'],
    close=df['close']
)])

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),
    specs=[[{'type': 'scatter'}, {'type': 'bar'}],
           [{'type': 'histogram'}, {'type': 'box'}]]
)

fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)
fig.add_trace(go.Histogram(x=data), row=2, col=1)
fig.add_trace(go.Box(y=data), row=2, col=2)

fig.update_layout(height=800, showlegend=False)

对于交互式 Web 应用程序，请使用 Dash（Plotly 的 Web 应用框架）：

uv pip install dash



import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

fig = px.scatter(df, x='x', y='y')

app.layout = html.Div([
    html.H1('Dashboard'),
    dcc.Graph(figure=fig)
])

app.run_server(debug=True)

plotly-express.md - 用于快速可视化的高级 API
graph-objects.md - 用于精细控制的低级 API
chart-types.md - 包含示例的 40 多种图表类型完整目录
layouts-styling.md - 子图、模板、颜色、自定义
export-interactivity.md - 导出选项和交互功能

🇺🇸English

Plotly

Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types.

Quick Start

Install Plotly:

uv pip install plotly

Basic usage with Plotly Express (high-level API):

import plotly.express as px
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 3, 4],
    'y': [10, 11, 12, 13]
})

fig = px.scatter(df, x='x', y='y', title='My First Plot')
fig.show()

Choosing Between APIs

Use Plotly Express (px)

For quick, standard visualizations with sensible defaults:

Working with pandas DataFrames
Creating common chart types (scatter, line, bar, histogram, etc.)
Need automatic color encoding and legends
Want minimal code (1-5 lines)

See reference/plotly-express.md for complete guide.

Use Graph Objects (go)

For fine-grained control and custom visualizations:

Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)
Building complex multi-trace figures from scratch
Need precise control over individual components
Creating specialized visualizations with custom shapes and annotations

See reference/graph-objects.md for complete guide.

Note: Plotly Express returns graph objects Figure, so you can combine approaches:

fig = px.scatter(df, x='x', y='y')
fig.update_layout(title='Custom Title')  # Use go methods on px figure
fig.add_hline(y=10)                     # Add shapes

Core Capabilities

1. Chart Types

Plotly supports 40+ chart types organized into categories:

Basic Charts: scatter, line, bar, pie, area, bubble

Statistical Charts: histogram, box plot, violin, distribution, error bars

Scientific Charts: heatmap, contour, ternary, image display

Financial Charts: candlestick, OHLC, waterfall, funnel, time series

Maps: scatter maps, choropleth, density maps (geographic visualization)

3D Charts: scatter3d, surface, mesh, cone, volume

Specialized: sunburst, treemap, sankey, parallel coordinates, gauge

For detailed examples and usage of all chart types, see reference/chart-types.md.

2. Layouts and Styling

Subplots: Create multi-plot figures with shared axes:

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))
fig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)

Templates: Apply coordinated styling:

fig = px.scatter(df, x='x', y='y', template='plotly_dark')
# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white

Customization: Control every aspect of appearance:

Colors (discrete sequences, continuous scales)
Fonts and text
Axes (ranges, ticks, grids)
Legends
Margins and sizing
Annotations and shapes

For complete layout and styling options, see reference/layouts-styling.md.

3. Interactivity

Built-in interactive features:

Hover tooltips with customizable data
Pan and zoom
Legend toggling
Box/lasso selection
Rangesliders for time series
Buttons and dropdowns
Animations

Custom hover template

fig.update_traces( hovertemplate='%{x} Value: %{y:.2f}<extra></extra>' )

Add rangeslider

fig.update_xaxes(rangeslider_visible=True)

Animations

fig = px.scatter(df, x='x', y='y', animation_frame='year')

For complete interactivity guide, see reference/export-interactivity.md.

4. Export Options

Interactive HTML:

fig.write_html('chart.html')                       # Full standalone
fig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file

Static Images (requires kaleido):

uv pip install kaleido



fig.write_image('chart.png')   # PNG
fig.write_image('chart.pdf')   # PDF
fig.write_image('chart.svg')   # SVG

For complete export options, see reference/export-interactivity.md.

Common Workflows

Scientific Data Visualization

import plotly.express as px

# Scatter plot with trendline
fig = px.scatter(df, x='temperature', y='yield', trendline='ols')

# Heatmap from matrix
fig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')

# 3D surface plot
import plotly.graph_objects as go
fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])

Statistical Analysis

# Distribution comparison
fig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)

# Box plot with all points
fig = px.box(df, x='category', y='value', points='all')

# Violin plot
fig = px.violin(df, x='group', y='measurement', box=True)

Time Series and Financial

# Time series with rangeslider
fig = px.line(df, x='date', y='price')
fig.update_xaxes(rangeslider_visible=True)

# Candlestick chart
import plotly.graph_objects as go
fig = go.Figure(data=[go.Candlestick(
    x=df['date'],
    open=df['open'],
    high=df['high'],
    low=df['low'],
    close=df['close']
)])

Multi-Plot Dashboards

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),
    specs=[[{'type': 'scatter'}, {'type': 'bar'}],
           [{'type': 'histogram'}, {'type': 'box'}]]
)

fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)
fig.add_trace(go.Histogram(x=data), row=2, col=1)
fig.add_trace(go.Box(y=data), row=2, col=2)

fig.update_layout(height=800, showlegend=False)

Integration with Dash

For interactive web applications, use Dash (Plotly's web app framework):

uv pip install dash



import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

fig = px.scatter(df, x='x', y='y')

app.layout = html.Div([
    html.H1('Dashboard'),
    dcc.Graph(figure=fig)
])

app.run_server(debug=True)

Reference Files

plotly-express.md - High-level API for quick visualizations
graph-objects.md - Low-level API for fine-grained control
chart-types.md - Complete catalog of 40+ chart types with examples
layouts-styling.md - Subplots, templates, colors, customization
export-interactivity.md - Export options and interactive features

Additional Resources

Official documentation: https://plotly.com/python/
API reference: https://plotly.com/python-api-reference/
Community forum: https://community.plotly.com/

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Plotly Python数据可视化教程：40+交互式图表创建与API选择指南

🇨🇳中文介绍

Plotly

快速开始

API 选择指南

使用 Plotly Express (px)

使用 Graph Objects (go)

相关 Skills

核心功能

1. 图表类型

2. 布局与样式

3. 交互性

自定义悬停模板

添加范围滑块

动画

4. 导出选项

常见工作流

科学数据可视化

统计分析

时间序列与金融

多图仪表板

与 Dash 集成

参考文件

其他资源

🇺🇸English

Plotly

Quick Start

Choosing Between APIs

Use Plotly Express (px)

Use Graph Objects (go)

Core Capabilities

1. Chart Types

2. Layouts and Styling

3. Interactivity

Custom hover template

Add rangeslider

Animations

4. Export Options

Common Workflows

Scientific Data Visualization

Statistical Analysis

Time Series and Financial

Multi-Plot Dashboards

Integration with Dash

Reference Files

Additional Resources

最新 Skills