用Python当中Plotly.Express模块绘制几张图表，真的被惊艳到了( 二 )

2025-12-25 Python

df = pd.DataFrame([dict(Task="Job A", StartDate='2009-01-01', FinishDate='2009-02-28', PorjectManager="小王"),dict(Task="Job B", StartDate='2009-03-05', FinishDate='2009-04-15', PorjectManager="小王"),dict(Task="Job C", StartDate='2009-02-20', FinishDate='2009-05-30', PorjectManager="小李")])fig = px.timeline(df, x_start="StartDate", x_end="Finish", y="Task", color="PorjectManager")fig.update_yaxes(autorange="reversed")fig.show()output

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当然项目的完成进度也可以在图表上面表示出来，代码如下

df = pd.DataFrame([dict(Task="Job A", StartDate='2009-01-01', FinishDate='2009-02-25', Completion_pct=60),dict(Task="Job B", StartDate='2009-03-05', FinishDate='2009-04-15', Completion_pct=40),dict(Task="Job C", StartDate='2009-02-20', FinishDate='2009-05-30', Completion_pct=75)])fig = px.timeline(df, x_start="StartDate", x_end="FinishDate", y="Task", color="Completion_pct")fig.update_yaxes(autorange="reversed")fig.show()

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Map在plotly.express模块当中绘制地图也是十分的简单，例如我们绘制的是地图当中的散点图，调用的是scatter_geo()方法，代码如下
df = px.data.gapminder().query("year == 2002")fig = px.scatter_geo(df, locations="iso_alpha",size="pop",)fig.show()output

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我们可以在此基础之上再进一步进行图表的美化，例如不同的国家表示不同的州用不同颜色的散点来表示，代码如下

fig = px.scatter_geo(df, locations="iso_alpha",color="continent",hover_name="country",size="pop",projection="natural earth")fig.show()

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以及分级统计图(Choropleth map) ，具体指的是在整个制图区域内，每个区划单元根据各分区划分出来的数量来进行分级，调用的是px.choropleth()方法

fig = px.choropleth(df, geojson=geojson, color="Bergeron",locations="district", featureidkey="properties.district",projection="mercator")fig.update_geos(fitbounds="locations", visible=False)fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})fig.show()

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