Using pyecharts in Python to Create Various Interactive Charts

pyecharts combines Python with Baidu's open‑source ECharts library, enabling developers to create rich, interactive data visualizations directly from Python code.

The article walks through the creation of many common chart types—bar, line, area, pie, rose, funnel, heatmap, scatter, 3D scatter, boxplot, map, geo heatmap, world map, word cloud, and a combined grid chart—showing how to import the required classes, generate sample data, configure chart options, and render the results in a notebook.

For each chart a self‑contained code snippet is provided; for example, a basic bar chart is built as follows:

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker

c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values())
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar‑MarkLine（自定义）"))
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False),
                     markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(y=50, name="yAxis=50")]))
)
c.render_notebook()

Similar patterns are used for other chart types, adjusting the chart class (e.g., Line() , Pie() , HeatMap() ), adding appropriate axes and series, and customizing global options such as title_opts , visualmap_opts , and legend_opts . The final combined chart demonstrates overlapping a bar and line chart on a shared grid with multiple Y‑axes.

All examples conclude with .render_notebook() to display the interactive chart within a Jupyter environment.