Creating Various Chart Types with Pyecharts in Python

This guide shows how to create many common chart types with pyecharts in Python.

Line chart :

from pyecharts.charts import Line
from pyecharts import options as opts
line = (
    Line()
    .add_xaxis(["周一", "周二", "周三", "周四", "周五", "周六", "周日"])
    .add_yaxis("系列1", [120, 200, 150, 80, 70, 110, 130])
    .set_global_opts(title_opts=opts.TitleOpts(title="折线图"))
)
line.render("line.html")

Bar chart :

from pyecharts.charts import Bar
bar = (
    Bar()
    .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
    .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
    .set_global_opts(title_opts=opts.TitleOpts(title="柱状图"))
)
bar.render("bar.html")

Scatter chart :

from pyecharts.charts import Scatter
scatter = (
    Scatter()
    .add_xaxis([1, 2, 3, 4, 5])
    .add_yaxis("series1", [10, 20, 30, 40, 50])
    .set_global_opts(title_opts=opts.TitleOpts(title="散点图"))
)
scatter.render("scatter.html")

Pie chart :

from pyecharts.charts import Pie
pie = (
    Pie()
    .add("", [list(z) for z in zip(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])])
    .set_global_opts(title_opts=opts.TitleOpts(title="饼图"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"))
)
pie.render("pie.html")

Map (China example) :

from pyecharts.charts import Map
map_chart = (
    Map()
    .add("商家A", [list(z) for z in zip(["北京", "上海", "广东"], [55, 60, 70])], "china")
    .set_global_opts(title_opts=opts.TitleOpts(title="地图"), visualmap_opts=opts.VisualMapOpts(max_=200))
)
map_chart.render("map.html")

Word Cloud :

from pyecharts.charts import WordCloud
words = [("Sam S Club", 10000), ("Macys", 6181), ("Amy Schumer", 4386)]
wordcloud = (
    WordCloud()
    .add("", words, word_size_range=[20, 100], shape='circle')
    .set_global_opts(title_opts=opts.TitleOpts(title="词云图"))
)
wordcloud.render("wordcloud.html")

Gauge :

from pyecharts.charts import Gauge
gauge = (
    Gauge()
    .add("", [("完成率", 66.6)])
    .set_global_opts(title_opts=opts.TitleOpts(title="仪表盘"))
)
 gauge.render("gauge.html")

Funnel :

from pyecharts.charts import Funnel
funnel = (
    Funnel()
    .add("商品", [list(z) for z in zip(["展现", "点击", "访问", "咨询", "订单"], [100, 80, 60, 40, 20])])
    .set_global_opts(title_opts=opts.TitleOpts(title="漏斗图"))
)
funnel.render("funnel.html")

Tree :

from pyecharts.charts import Tree
data = [{"name": "树节点", "children": [{"name": "子节点1"}, {"name": "子节点2", "children": [{"name": "孙子节点"}]}]}]
tree = (
    Tree()
    .add("", data)
    .set_global_opts(title_opts=opts.TitleOpts(title="树图"))
)
tree.render("tree.html")

Parallel coordinates :

from pyecharts.charts import Parallel
parallel_data = [("一部", 99, 92, 95, 95), ("二部", 99, 92, 95, 95)]
parallel = (
    Parallel()
    .add_schema([
        opts.ParallelAxisOpts(dim=0, name="部门"),
        opts.ParallelAxisOpts(dim=1, name="综合素质", type_="value"),
        opts.ParallelAxisOpts(dim=2, name="学科1", type_="value"),
        opts.ParallelAxisOpts(dim=3, name="学科2", type_="value"),
        opts.ParallelAxisOpts(dim=4, name="学科3", type_="value"),
    ])
    .add("parallel", parallel_data)
    .set_global_opts(title_opts=opts.TitleOpts(title="平行坐标系图"))
)
parallel.render("parallel.html")

Sankey diagram :

from pyecharts.charts import Sankey
sankey_data = {"nodes": [{"name": "节点1"}, {"name": "节点2"}, {"name": "节点3"}, {"name": "节点4"}], "links": [{"source": "节点1", "target": "节点2", "value": 10}, {"source": "节点2", "target": "节点3", "value": 20}, {"source": "节点3", "target": "节点4", "value": 30}]}
sankey = (
    Sankey()
    .add("sankey", nodes=sankey_data["nodes"], links=sankey_data["links"], linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source"), label_opts=opts.LabelOpts(position="right"))
    .set_global_opts(title_opts=opts.TitleOpts(title="桑基图"))
)
sankey.render("sankey.html")

Geo chart :

from pyecharts.charts import Geo
geo = (
    Geo()
    .add_schema(maptype="china")
    .add("geo", [("北京", 55), ("上海", 60), ("广州", 70)])
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="地理坐标系图"))
)
geo.render("geo.html")

Timeline (bar chart over years) :

from pyecharts.charts import Timeline, Bar
timeline = Timeline()
for i in range(2015, 2020):
    bar = (
        Bar()
        .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
        .add_yaxis("商家A", [5*i, 20*i, 36*i, 10*i, 75*i, 90*i])
    )
    timeline.add(bar, f"{i}年")

timeline.render("timeline.html")

3D Scatter (Scatter3D) :

from pyecharts.charts import Scatter3D
from pyecharts import options as opts
data = [[1,1,1],[2,2,2],[3,3,3],[4,4,4],[5,5,5]]
scatter3d = (
    Scatter3D()
    .add("", data)
    .set_global_opts(title_opts=opts.TitleOpts(title="3D 散点图"), visualmap_opts=opts.VisualMapOpts(range_color=['#313695','#4575b4','#74add1','#abd9e9','#e0f3f8','#ffffbf','#fee090','#fdae61','#f46d43','#d73027','#a50026']))
)
scatter3d.render("scatter3d.html")

3D Bar (Bar3D) :

from pyecharts.charts import Bar3D
from pyecharts import options as opts
data = [[i, j, i * j % 100] for i in range(20) for j in range(20)]
bar3d = (
    Bar3D()
    .add("", [[d[1], d[0], d[2]] for d in data], xaxis3d_opts=opts.Axis3DOpts(type_="category", data=[str(i) for i in range(20)]), yaxis3d_opts=opts.Axis3DOpts(type_="category", data=[str(i) for i in range(20)]), zaxis3d_opts=opts.Axis3DOpts(type_="value"))
    .set_global_opts(title_opts=opts.TitleOpts(title="3D 柱状图"), visualmap_opts=opts.VisualMapOpts(max_=100, min_=0, range_color=['#313695','#4575b4','#74add1','#abd9e9','#e0f3f8','#ffffbf','#fee090','#fdae61','#f46d43','#d73027','#a50026']))
)
bar3d.render("bar3d.html")

3D Surface (approximated with Bar3D) :

from pyecharts.charts import Bar3D
from pyecharts import options as opts
import numpy as np
x_data = y_data = list(range(-5, 5))
z_data = [[y, x, np.sin(np.sqrt(x**2 + y**2))] for x in x_data for y in y_data]
surface3d = (
    Bar3D()
    .add("", z_data, shading="color", xaxis3d_opts=opts.Axis3DOpts(type_="value"), yaxis3d_opts=opts.Axis3DOpts(type_="value"), zaxis3d_opts=opts.Axis3DOpts(type_="value"))
    .set_global_opts(title_opts=opts.TitleOpts(title="3D 曲面图（近似）"), visualmap_opts=opts.VisualMapOpts(max_=1, min_=-1, range_color=['#313695','#4575b4','#74add1','#abd9e9','#e0f3f8','#ffffbf','#fee090','#fdae61','#f46d43','#d73027','#a50026']))
)
surface3d.render("surface3d.html")

Each code block creates the chart object, configures its data and visual options, and finally renders an HTML file that can be opened in a browser to view the interactive visualization.