散点图

目前，Plotly 的高级绘图子模块 express 支持35 种常见图形，各图形参数见此，帮助文档见此。

• Basics: scatter, line, area, bar, funnel, timeline
• Part-of-Whole: pie, sunburst, treemap, icicle, funnel_area
• 1D Distributions: histogram, box, violin, strip, ecdf
• 2D Distributions: density_heatmap, density_contour
• Matrix or Image Input: imshow
• 3-Dimensional: scatter_3d, line_3d
• Multidimensional: scatter_matrix, parallel_coordinates, parallel_categories
• Tile Maps: scatter_mapbox, line_mapbox, choropleth_mapbox, density_mapbox
• Outline Maps: scatter_geo, line_geo, choropleth
• Polar Charts: scatter_polar, line_polar, bar_polar
• Ternary Charts: scatter_ternary, line_ternary

c(
  "scatter", "density_contour", "density_heatmap",
  "line", "area", "bar", "timeline", "histogram",
  "histogram", "violin", "box", "strip", "scatter_3d",
  "line_3d", "scatter_ternary", "line_ternary",
  "scatter_polar", "line_polar", "bar_polar", "choropleth",
  "scatter_geo", "line_geo", "scatter_mapbox",
  "choropleth_mapbox", "density_mapbox", "line_mapbox",
  "scatter_matrix", "parallel_coordinates",
  "parallel_categories", "pie", "sunburst", "treemap",
  "icicle", "funnel", "funnel_area"
)

express 支持快速地绘制图形，使用接口类似 R 语言，从 R 语言切换过来简单，和统计模型结合的非常好，以分组线性回归散点图为例。

import plotly.express as px

px.scatter(
    px.data.iris(),
    x="sepal_width",
    y="sepal_length",
    color="species",
    trendline="ols",
    template="simple_white",
    labels={
        "sepal_length": "Sepal Length (cm)",
        "sepal_width": "Sepal Width (cm)",
        "species": "Species of Iris",
    },
    title="Edgar Anderson's Iris Data",
    color_discrete_sequence=px.colors.qualitative.Set2
)

导出图形非常简单，保存绘图对象，然后调函数 write_image() 导出即可。

import plotly.express as px
fig = px.scatter(
    px.data.iris(),
    x="sepal_width",
    y="sepal_length",
    color="species",
    trendline="ols",
    template="simple_white",
    labels={
        "sepal_length": "Sepal Length (cm)",
        "sepal_width": "Sepal Width (cm)",
        "species": "Species of Iris",
    },
    title="Edgar Anderson's Iris Data",
    color_discrete_sequence=px.colors.qualitative.Set2,
)
fig.write_image("img/iris.png", engine="kaleido")
fig.write_image("img/iris.pdf", engine="kaleido")
fig.write_image("img/iris.svg", engine="kaleido")

支持导出多种格式，且导出的图片效果很好，如图1所示。

图 1: Plotly 导出高质量的图片

气泡动画

plotly 内置了著名的 gapminder 数据集，如下：

import plotly.express as px
df = px.data.gapminder()
df.head(6)
#        country continent  year  ...   gdpPercap  iso_alpha  iso_num
# 0  Afghanistan      Asia  1952  ...  779.445314        AFG        4
# 1  Afghanistan      Asia  1957  ...  820.853030        AFG        4
# 2  Afghanistan      Asia  1962  ...  853.100710        AFG        4
# 3  Afghanistan      Asia  1967  ...  836.197138        AFG        4
# 4  Afghanistan      Asia  1972  ...  739.981106        AFG        4
# 5  Afghanistan      Asia  1977  ...  786.113360        AFG        4
# 
# [6 rows x 8 columns]

相比于 R 包 gapminder 内置的数据集，Python 版新加了一列 iso_alpha。

import plotly.express as px

df = px.data.gapminder()
px.scatter(
    df,
    # 横轴
    x="gdpPercap",
    # 纵轴
    y="lifeExp",
    # 动画帧
    animation_frame="year",
    # 动画分组
    animation_group="country",
    # 气泡大小映射到总人口
    size="pop",
    # 颜色映射到地区/大洲
    color="continent",
    # 悬浮
    hover_name="country",
    # 图形主题
    template="none",
    # 横轴坐标取对数
    log_x=True,
    size_max=55,
    # 横轴范围
    range_x=[100, 100000],
    # 纵轴范围
    range_y=[25, 90],
    # 动画标题
    title = "各国人均寿命与人均GDP关系演变",
    # 设置横纵坐标轴标题
    labels={
        "gdpPercap": "人均 GDP (美元)",
        "lifeExp": "人均寿命 (岁)",
        "year": "年份",
        "continent": "大洲",
        "country": "国家",
        "pop": "人口总数"
    },
    # 调用来自 colorbrewer 的 Set2 调色板
    color_discrete_sequence=px.colors.colorbrewer.Set2
)

类似 ggplot2 包的主题设置，Plotly Express 也是支持 自定义风格样式的，其中图形主题 最为方便快捷。默认主题为 plotly，可用的有：

import plotly.io as pio
pio.templates
# Templates configuration
# -----------------------
#     Default template: 'plotly'
#     Available templates:
#         ['ggplot2', 'seaborn', 'simple_white', 'plotly',
#          'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
#          'ygridoff', 'gridon', 'none']

只需将 template="none" 替换为以上任意一种，即可获得不一样的效果。

import plotly.express as px
df = px.data.gapminder()
px.scatter(
    df,
    # 横轴
    x="gdpPercap",
    # 纵轴
    y="lifeExp",
    # 动画帧
    animation_frame="year",
    # 动画分组
    animation_group="country",
    # 气泡大小映射到总人口
    size="pop",
    # 颜色映射到地区/大洲
    color="continent",
    # 悬浮
    hover_name="country",
    # 图形主题
    template="ggplot2",
    # 横轴坐标取对数
    log_x=True,
    size_max=55,
    # 横轴范围
    range_x=[100, 100000],
    # 纵轴范围
    range_y=[25, 90],
    # 动画标题
    title = "各国人均寿命与人均GDP关系演变",
    # 设置横纵坐标轴标题
    labels={
        "gdpPercap": "人均 GDP (美元)",
        "lifeExp": "人均寿命 (岁)",
        "year": "年份",
        "continent": "大洲",
        "country": "国家",
        "pop": "人口总数"
    },
    # 调用来自 colorbrewer 的 Set2 调色板
    color_discrete_sequence=px.colors.colorbrewer.Set2
)

除了主题模版，plotly 模块还内置了很多调色板，比如家喻户晓的 colorbrewer 调色板，见下图。

import plotly.express as px
fig = px.colors.colorbrewer.swatches()
fig.show()

地图动画

参考 plotly 官网 choropleth map

import plotly.express as px
# 提取 2007 年的数据
df = px.data.gapminder().query("year == 2007")
# 2007 年世界平均寿命
avg_lifeExp = (df["lifeExp"] * df["pop"]).sum() / df["pop"].sum()
# 绘制地区分布图
px.choropleth(
    df,
    locations="iso_alpha",
    color="lifeExp",
    color_continuous_scale=px.colors.diverging.BrBG,
    color_continuous_midpoint=avg_lifeExp,
    title="2007 年世界平均寿命 %.1f（岁）" % avg_lifeExp,
    labels={
        "iso_alpha": "国家编码",
        "lifeExp": "人均寿命 (岁)"
    }
)

与气泡动画类似，只需指定 animation_frame 和 animation_group 的参数值，就可以生成动画。

import plotly.express as px
df = px.data.gapminder()
px.choropleth(
    df,
    locations="iso_alpha",
    color="lifeExp",
    # 动画帧
    animation_frame="year",
    # 动画分组
    animation_group="country",
    # 悬浮提示
    hover_name="country", 
    color_continuous_scale=px.colors.sequential.Viridis,
)